Topcon and Bentley have joined efforts to provide opportunities designed to allow construction industry professionals to learn best practices in constructioneering, a process of managing and integrating survey, engineering and construction data, to streamline construction workflows and improve project delivery.
“The courses are designed in a dialogue format to allow Topcon and Bentley personnel to interact directly with attendees to cater the experience for their specific questions and demands,” said Ron Oberlander, senior director of Topcon Professional Services. “The future of construction automation continues to move forward with constructioneering digital workflows, which make the work of surveyors, engineers, and construction professionals automated, continuous, and continuously more valuable, throughout project lifecycles and beyond completion.”
“Topcon and Bentley’s federated constructioneering technologies enable firms to gain unprecedented digital visibility and insights into their project outcomes, as compared to traditional construction workflows. Attendees of our Constructioneering Academy will learn how their organizations can improve project delivery by leveraging constructioneering technology, methods, and best practices to execute their projects more efficiently, monitor construction performance and progress, and reduce project costs,” said Vinayak Trivedi, Bentley Institute vice president.
The Constructioneering Academy will continue with additional sessions throughout learning centers located worldwide designed to reach industry professionals with hands-on training in real-world scenarios and workflows.
Tersus GNSS Inc., a GNSS positioning solution provider, has introduced three new GNSS kits. The BX305, BX306 and BX316 HRS kits feature high-precision BX305, BX306 and BX316 GNSS RTK boards.
The HRS kits consist of RTK receivers, GNSS antennas, RS05R radio station modems, radio station antennas, and related cables and converters.
Tersus GNSS BX305-HRS kit.
Tersus GNSS BX306-HRS kit.
Tersus GNSS BX316-HRS kit.
Embedded in the receivers are the Tersus RTK boards. They are compact-design, energy-efficient, centimeter-level accurate GNSS real-time kinematic (RTK) boards, bringing high-precision positioning accuracy to the market, the company said.
Different from the standard BX305/306/316 GNSS kits, the new HRS versions are equipped with RS05R, lightweight and robust UHF, which is a rover radio solution for wireless application.
It provides reliable data communication for demanding conditions that require a combination of stability, high performance and long-range operation.
With complete components and accessories in the kits, they can be used in a variety of applications, such as unmanned aerial vehicle (UAVs), surveying, mapping, precision agriculture, construction engineering and deformation monitoring.
Spoofing — the generation of false and misleading GPS signals by “bad actors” — is becoming an increasing problem for all GPS users, and surveyors just as much as everyone else should be knowledgable and take countermeasures.
Javad GNSS has announced that spoofer detection is now available on all of its OEM boards. If the receivers equipped with such boards detect more than one correlation peak for any PRN code, they warn the user of the presence of spoofing (false signals) and identify the spoofed satellites.
The receivers then switch to other signals and sensors that are not being spoofed, to maintain accurate positioning. The user can also employ the receiver to try to identify the direction from which the spoofing signals are originating.
Over the past two years, I’ve been sharing my view on land surveying over a variety of topics. One of the constant themes I try to maintain is technological improvements and how surveyors need to continue to embrace new applications and equipment.
While I will also argue that we cannot forget our surveying roots (see GPS World, March 2017), we still need to keep an eye on future technologies, means and methods to increase our productivity and profitability as well.
With this idea of peeking at the road ahead, I traveled to Berlin, Germany, to cover Intergeo 2017, an international trade show for everything geospatial.
Held every year at different venues around Germany, Intergeo is the world’s largest conference and exhibition for geodesy, geoinformation and land management. The theme of the conference this year was “We are bringing worlds together,” and based upon the number of vendors, new equipment and applications, and record breaking attendance, it was quite evident they hit their mark.
The numbers were astounding: 580 vendors from 37 countries, 18,000+ visitors from 100+ countries, all packed in six interlinking halls covering more than 325,000 square feet (7.5+ acres). When I spoke with several contemporaries who previously attended Intergeo, all warned me to be ready for the size and scale of the facility, the number of vendors, and the large spaces occupied by the big survey players. Truly thinking this rhetoric was hyperbole, I went with guarded expectations. Boy, was I in for big surprise.
Walking up to the entrance, one could sense immediately the size and enormous presence of this conference. The registration lines were easy and efficient, with plenty of entry gates and attendants to help with any information. When I entered the first hall for my initial visit, the prior warnings about the size and scale were quite true. This conference was three days long, and I knew I was going to need every minute to cover all the bases.
My account here is broken into four sections — one hall per day. Leica, NovAtel, Laser Technology and Septentrio were among the first booths I encountered on day one. Javad, ComNav, Hemisphere and Emlid were in the next hall over, and can be found below “Behind Door Number Two.” Day three found me hobnobbing with CHC Navigation, EOS and Swift Navigation; look for them under “I’ll Take Hall Three for $200, Alex.” I topped off my Intergeo experience interviewing NavCom, Tersus and Trimble in “The Big Finish (Or Is It?)”
ENTERING ANOTHER DIMENSION…
Coming into Hall 1.1, the first thing that catches one’s eye besides the vastness of the entire space is the size and depth of the Hexagon exhibit. Hexagon is the parent company of Leica, NovAtel, and several others, and all were there displaying new products and software for a multitude of geospatial needs.
I was introduced to the new Leica GS18T GNSS RTK rover (Leica Geosystems debuts GNSS RTK rover at Intergeo 2017) with many new upgrades, including being able to measure a point while the unit is not being held plumb. Yes, you read that right; the unit has an inertial measurement unit (IMU) built in that compensates for any rod tilt and corrects the location back to the bottom of the rod. Now it is possible to collect a GNSS location to places and objects no longer thought possible.
This feature has been available with the “J-Tip” from Javad for the past year: now Leica has added the capability to the GS18T.
While the demonstration took care of my skepticism of the unit, it raised different concerns with the field personnel using it. This method of “no bubble” is fine for this unit, but I can envision crews getting sloppy with conventional GNSS and total station prism rods when mixed with this new technology. This will increase the need for proper training and trust that the all rod-based equipment is used in its intended manner and procedure.
NovAtel was on hand with presentation of a full line of GPS receivers, boards and peripherals, including its GAJT line of anti-jamming receivers as well as an “interference” toolkit of analyzing software modules. With the solid product line and software, the company motto of “assured positioning” bodes well for its users.
Laser Technology Inc. has provided many measurement breakthroughs over the past few decades, and its introduction of the TruPoint 200h is no exception (see Laser Technology highlights TruPoint 200h at Intergeo 2017). The company has taken the TruPoint 200 line of measuring devices and expanded its capability with phase shift and pulse diode measurements, which means the unit will determine the most accurate method based upon reflectivity. This is also paired with a data collection and reporting application on your Android smartphone to greatly expand your ability to share your data.
Also at Intergeo introducing a new GNSS receiver was Septentrio, a navigation systems developer from Belgium. Septentrio was showing the Altus NR3, a lightweight multi-constellation receiver aimed at the surveying and mapping community. This new unit boasts an AIM+ system designed to monitor and protect the user’s data from jamming and spoofing so the collected data is confidently accurate.
Carlson displayed its new BRx6 multi-GNSS receiver along with the RT3 tablet data collector. The BRx6 receiver expands the Carlson family of GNSS products to include connection to most RTN systems worldwide as well as the Atlas L-band correction service that is beginning to gain many followers. Paired with the new RT3 tablet, these products continue the look, feel and service that Carlson users have enjoyed for many years.
Sokkia was there with a full complement of surveying and monitoring equipment, with the GCX3 GNSS receiver being the main focus of their new products. This unit sets the bar even higher for light and efficient GNSS receivers as it weighs only 440 grams with the batteries, yet is more capable than many similar systems on the market.
Another new GNSS receiver introduction was from Hi-Target, the iRTK-5. This new model is the one of the first units to have an OLED touchscreen on the receiver. It supports reception from all major GNSS constellations as well as the L-band correction service. Hi-Target prides itself on a new proprietary differential correction technology that analyzes the integrity of data from all sources before providing a position. This model also has a 4G LTE chipset that will communicate with almost all cellular systems worldwide.
One thing that stood out to me that differs greatly from surveying in the U.S. is the proliferation of monuments and monitoring points/devices used by surveyors throughout the world. So, the multitude of vendors offering varying kinds of targets, prisms, and survey point markets should not have shocked me, but it still did. It was quite impressive with the walls of targets and prism assemblies for many different applications along with the tables of nails, tablets, monuments and vault systems used by surveyors worldwide. Among the notables were Rothburcher Systeme and Bohnenstingl, who both offer a complete line of products beyond most surveyor’s imaginations.
The Juniper Systems booth showcased the Mesa2 rugged tablet and the Geode sub-meter GNSS receiver, both designed with efficient mapping collection in mind at an affordable price point, yet rugged enough to take on most environments. They were also highlighting their CT5 rugged smartphone and CT7G rugged tablet as solid products for any surveying and mapping data collection need.
BEHIND DOOR NUMBER TWO
After navigating the first of four conference areas, I wondered if all the remaining halls would be just as impressive. Hall 2 did not disappoint, as the exhibit spaces were just as big and remarkable as the first one.
Already a major entity within the GNSS community, Javad came to Intergeo 2017 to introduce its entry into the UAV market, the Triumph F-1. Unlike other UAVs, this unit was designed starting with the GNSS engines Javad is known for; engineers then built the flying craft around the brains of the system. It utilizes the same DNA of the Triumph-LS receiver along with ease of Javad software, all on a multi-rotor platform with hot-swappable batteries.
Aimed for more agricultural users, ComNav introduced a lightweight GNSS receiver, the T30, and a new software guidance system compatible with most tractor configurations. This system is designed to be more efficient and precise than OEM tractor guidance as well as more customizable for the user.
The company behind the new Atlas L-band correction service, Hemisphere GNSS, also provided introductions to several new products at Intergeo. In addition to several navigation chipsets for OEM use, they also introduced the 321+ GNSS Smart Antenna. This new receiver is multi-frequency and multi-GNSS with an Athena RTK engine and Atlas L-band global corrections to cover most positional needs. With hot-swappable batteries, this unit will run over 12 hours on two sets that are provided.
EMLID may be a newcomer to the GNSS environment but they are making noise by offering new products and technology at price points for more consumers. At Intergeo, they were demonstrating the Reach RS GNSS receiver with RTK capability, the Reach GNSS module for UAV systems and the Edge module, an advanced drone controller with HDMI video input and 5.8 GHz data link. These guys are my sleeper pick for becoming a bigger player in the very near future.
Topcon’s space, which included industry partner Intel included an interactive dome using their “Immersive Point Cloud Workspace” software and 3D point cloud data to give attendees a virtual reality tour of sites previously mapped with Topcon equipment and software. This four-meter dome was a popular stop with visitors, as was the product introductions of their SmoothRide software and the MAGNET Collage desktop mass data processing software. In addition, another product showcased in conjunction with Intel was their UAV systems, led by the Sirius Pro fixed wing vehicle and the Falcon 8 multi-rotor vehicle.
The main presentation from Bentley to the surveying/GNSS community was the increased capability of their ContextCapture software module. With 3D point clouds and virtual reality systems become the norm, Bentley has upped their game with visualization tools and features within this module not found in many point cloud software packages. The biggest improvement is the ability for survey-grade data acquired through laser scanning, LiDAR and photogrammetry to be easily input and manipulated for many different uses. From BIM to roadway corridor modeling and asset management, these improvements are making the utilization of 3D data more seamless.
Something that caught my eye initially as a novelty was TinyMobileRobots, a Danish company producing a small unmanned cart for marking surfaces. They currently have three products: the TinySurveyor, the TinyLineMarker, and the TinyPreMarker. All have GNSS receivers that operate autonomously on a predetermined route and carry a paint can for marking specific points. From marking athletic fields to paint striping, they might be on to something quite unique but very cool.
I’LL TAKE HALL THREE FOR $200, ALEX
Hall 3 brought us to CHC Navigation and their full range of surveying and mapping products. Highlighted during our discussions with them was the M6, i70 and i80 GNSS receivers and the Apache 5 USV boat for hydrographic applications. It was quite evident how large of a company CHC is and how vast their global presence is so don’t be surprised to see them in North America soon.
EOS Positioning Systems is another smaller GNSS receiver producer that is targeting the mapping community with the lower entry pricing and smartphone application interfacing. From the basic L1 receiver for mapping to the multi-constellation, triple-frequency model including Atlas L-band corrections, EOS is providing an easy to use package at an affordable price point.
Another entry in the OEM board/system provider of centimeter-level accuracy systems is Swift Navigation and their Piksi line of products. From agricultural applications to autonomous vehicle positioning, they are providing navigational systems that will guide our future. As the surveying community expands its use of unmanned vehicles, I would not be surprised to see Swift at the forefront of this effort.
Another member of the Hexagon family, the Geomax display was loaded with everything imaginable for measuring and remote sensing. The main introduction for Intergeo 2017 was X-PAD Office Fusion, an all-in-one software package that allows the data from various sources to come together in one place for creating and manipulating 3D models. While I was not familiar with Geomax, it was quite evident that their customer base, while European-centric, was big and quite loyal to the brand.
A newcomer to the surveying world is Pix4D and their photo processing software. Used by many UAV enthusiasts and now surveyors, Pix4D has quickly become one of the largest providers of software for creating orthometric photos that are georeferenced to known coordinate systems and GIS databases. Most of their success has been because of ease of use and affordability versus the leading surveying software packages. They are also industry partners with Esri and DJI, so having big friends in popular places has helped their cause. Look for more great stuff from them for surveying applications in the near future.
THE BIG FINISH
As I entered Hall 4, I began to wonder if I was done with the major attractions and getting into the bit players. I was wrong.
The first stop was Geozone, who was introducing a new receiver, Falcon SF, through a collaboration with NavCom. This new unit features multi-constellation and multi-frequency collection, but also includes Starfire, a global system designed and maintained by NavCom that is a satellite-based correction signal and provides accuracy of 5 CM anywhere in the world. This correction system comes standard with a subscription when you purchase the receiver, which is unheard of in most surveying environments. This is another product I predict that will be making more of a global presence in short time.
Tersus GNSS was at Intergeo to introduce many OEM navigation boards and an RTK system for surveyors and autonomous control systems. The Tersus David RTK system is designed to turn your smartphone into a high accuracy GNSS data collector. This system is highly durable and compact so it will accommodate many different mapping needs.
Another company that has global reach but not much exposure to U.S. business, South Surveying & Mapping Instruments, wouldn’t give an attendee the impression that they are a small player in the surveying world. Their exhibit space was impressive, ranging from simple theodolites to high accuracy robotic total stations, RTK GNSS receivers and fixed-wing/multi-rotor UAVs.
As lidar technology becomes more available and affordable, a stop at the Riegl booth was a must. They were introducing many new products at the Intergeo 2017 show, including the miniVUX-1DL UAV laser scanner for multi-rotor and fixed-wing aircraft. This little beauty weighs under 3 kg and fits on most scalable UAV platforms.
Another fun item on display was the VMX-2HA Dual Scanner Mobile Laser Scanner, which looks like a high-tech octopus on top of your vehicle. This unit collects 2 million measurements per second as well as 9 x 12MP camera images at various angles. As the miniaturization of technology continues, I see RIEGL continuing to lead the lidar segment well into the future.
Just when I thought I was almost done, I realized that there was one major player left that I had not seen: Trimble. They were in the back of Hall 4 across almost the entire width of the conference space. Everything geomatics, remote sensing, and navigation; it was here in one of the biggest exhibition spaces at Intergeo 2017.
All the latest surveying instruments were here, including the R10 GNSS receiver and SX10 scanning total station. The new items for surveying at this show were numerous; the C3 and C5 mechanical total stations with autofocus, Catalyst software with GNSS receiver for smartphones, the T10 rugged tablet for survey and GIS applications, and OEM receiver boards (BD940-INS, BD992-INS and BD990).
Also included within the Trimble space were Nikon and Spectra Precision branded instruments as well as the latest acquisition, Applanix GNSS-aided inertial movement systems. While Trimble has grown considerably in the past decade, it seems as nothing will slow them down. If they continue to introduce great products and technology, I wouldn’t bet against them.
BUT WAIT, THERE’S MORE…
There were three more halls, with two of them being organizations and information booths. Hall 6, however, has grown into a standalone space as “Interaerial Solutions,” Europe’s biggest UAV show. All the main players were in here (including DJI, DroneDeploy and over 150 more) so almost everything imaginable with UAVs can be found here.
During the conference, the adjacent courtyard was utilized for UAV demonstrations and product introductions. It will be interesting to see in the coming years if this segment of measurement and remote sensing will continue to expand with number of vendors/suppliers or if it will get absorbed by many of the bigger players within the geomatics community.
INTERGEO 2017, IN RETROSPECT
The common theme/message through this gathering was digitalization, “smart cities” and the evolution of the occupations that work within these environments. Intergeo is an impressive gathering of likeminded people discussing how to manage the increasing waves of data through technology, analyzation and thought processing.
While I can’t say that these types of gathering do not happen in the U.S., it is not as obvious as the annual assembly of Intergeo in Germany. The surveying community in the United States needs to hold a similar “summit” to help guide the profession toward its future goals. The one thing I have always appreciated about surveying is how it does embrace technology and forward thinking yet must rely on the past to tell us where we have been.
Digitalization is here and cities will get smarter with or without us, so it’s up to us as surveyors to keep looking forward with the times. Global measurement and navigation will be a big part of that, so let’s put our thinking caps on to see what we learn next.
Until next time, guten tag und gute gesundheit.
A big thank you also goes out to my fellow Intergeo members Ryan Gerard, Mike Joyce, and Allison Barwacz for making this a wonderful experience.
Velodyne Lidar Inc., maker of 3D vision systems for autonomous vehicles, is partnering with YellowScan to integrate its VLP-16 Puck and VLP-16 Puck LITE lidar sensors into YellowScan’s Surveyor.
The result is a turn-key and reliable lidar system for demanding UAV applications, the companies said.
Real-time lidar systems for UAVs are used around the world for industrial and scientific applications, including surveying, civil engineering, archeology and environmental science.
By combining its LiveStation app with the real-time 3D data capture capabilities of Velodyne’s VLP-16 Puck and VLP-16 Puck LITE sensors — both of which feature a 360-degree horizontal field-of-view, 100-meter range, and weigh 830 grams and 590 grams, respectively — YellowScan delivers a turn-key surveyor system that can be mounted to any drone for short-time data processing needs.
The result is a real-time in-flight lidar monitoring platform, with users able to see how the final map is being generated in real-time during the drone mission, and the basic map datasets available immediately after the mission.
“YellowScan is known for its commitment to providing reliable and easy to use sensing solutions for the UAV industry, which make the VLP-16 Puck sensors an easy choice for the Surveyor system,” said Erich Smidt, executive director, Europe, Velodyne Lidar. “The VLP-16 Pucks are some of our newest offerings, with significant effort put into reducing weight while maintaining the resolution and reliability expected of Velodyne’s industry-leading lidar sensors.”
“YellowScan Surveyor, the turn-key lidar solution integrating Velodyne’s advanced VLP-16 sensor, enables mapping professionals to do more in less time thanks to tremendously high density and accurate measurements acquired from UAVs,” said Tristan Allouis, CTO of YellowScan.
Professional drone company Wingtra is partnering with photogrammetry company Pix4D. Pix4D’s software suite is now available to WingtraOne users, both directly and via Wingtra’s distributors.
WingtraOne, Wingtra’s main product, is a vertical take-off and landing (VTOL) UAV that enables data collection for a variety of industries. The partnership with Pix4D aims to augment its status with an end-to-end solution including 2D map and 3D model construction from aerial data.
The WingtraOne drone bridges the gap between traditional multi-rotors and fixed-wing drones, the company said. It takes off and lands vertically like conventional multirotors, but once in flight, the drone tilts forward to fly like a fixed-wing aircraft.
Being able to carry heavy payload such as the Sony RX1RII, the drone offers high mapping accuracy, while covering an area of 980 acres (400 Ha) at 3 cm/px (1.2 in/px) GSD or the equivalent of 570 football fields.
The WingtraOne is available in use in Europe, China, the United States and Australia for applications ranging from surveying and precision agriculture to glacier monitoring.
Wingtra (booth 109) and Pix4D (booth 415) are exhibiting at Commercial UAV Expo Americas, which takes place Oct. 24-26 in Las Vegas.
Map made by Pix4D pictures taken by WingtraOne with RX1RII camera. (image: Wingtra)
Turning Information into Insight. Wingtra’s diverse user base is complemented by Pix4D, whose product range is aimed at the surveying and agriculture industry, among others.
Pix4D has allows professionals to generate high-quality point clouds, orthomosaics, surface and terrain models from aerial imagery. Some of its popular offerings include Pix4Dmapper for precisely georeferenced 2D maps and 3D models, and Pix4Dag for accurate reflectance and index maps (NDVI, NDRE).
With WingtraOne’s autonomous aerial data collection and Pix4D’s advanced data-analysis capabilities offered as a single bundle, professional users can now expect a plug-and-play solution. “We are keen on collaborating strongly in our upcoming events. Actually we are meeting very soon at UAV Expo in Las Vegas,” Bailey said.
“The bond between the companies was established some time ago, since realizing the potential of pairing high-resolution aerial images with cutting-edge photogrammetry modeling software,” said Caroline Bailey, Pix4D regional sales manager for Europe. “We are very happy to announce the decision to become official partners.”
Leopold Flechsenberger, sales manager at Wingtra, added, “We have always aimed at providing the best survey-grade aerial imagery to our users, so Pix4D was an obvious choice from the start. From now on, Wingtra is offering a reduced price on WingtraOne drones, when bundled with Pix4Dmapper.”
Drones are quickly becoming a staple of the maritime industry. In January, the European Maritime Safety Agency (EMSA) issued the largest ever civilian maritime drone contact, valued at €67 million.
Under the contract, drones will be used to assist with border control, search-and-rescue operations and monitoring of pollution, as well as the detection of illegal fishing and drug and people trafficking.
External Vessel Inspections. Big names in the maritime industry such as DNV-GL, Lloyds Register and Maersk have all shown strategic intent to revolutionize their operations by embracing drone technology, and many maritime operators are now following suit.
All ship owners know that traditional methods of external vessel inspection can be a costly affair. Now that high-definition, camera-equipped drones are widely available and affordable, it is becoming more common to use them for external vessel inspections to assess structural conditions. Identifying substantial corrosion, significant deformation, fractures, damage or other structural deterioration can be done quickly, easily and cost-effectively using drones.
Tank Inspections. The visual inspection of cargo tanks was traditionally performed by workers suspended on ropes to inspect the tank structure. The sheer size of modern-day vessels means that access methods including staging, rafting and climbing are often used by surveyors to access tanks.
In contrast, drone surveys require no human access to the tank and, since no access equipment is required, there are no setup costs, and inspections can be completed within a quicker timeframe.
Martek Marine’s V-200 UAS. (Photo: Martek Marine)
Bathymetric Surveys. Accurate and reliable information on the features of water bodies and their shorelines is vital to navigational safety. Bathymetric surveys gather the information, which is then published for use on nautical charts. Rather than using a fixed-wing airplane or helicopter, bathymetric sensors developed for drones allow this type of survey to be carried out flexibly and at a fraction of the cost.
To operate effectively in the harsh maritime environment, the technology has been developed to withstand storm force wind and heavy rain, snow and salt spray.
As technology advances, so does the flight time available on drones, meaning more area can be covered in a quicker timeframe.
Floating Flare-Tip Inspections. Drone surveys typically exist to provide close visual and thermal inspections of high, live or difficult to access structures offshore, and there’s nothing more challenging to access than a flare tip, 70 meters above water, on a floating production facility.
Drone survey inspections for flare tips remove the need for a shutdown to inspect the flare and offer reduced costs compared to aerial surveys carried out by helicopter or plane.
Offshore Wind Energy. The wind energy sector is growing fast. Storm force winds, erosion, lightning strikes and even build-up of insects can have an impact on turbines, and blades need to be inspected for deterioration. Inspectors have traditionally had to scale the turbines with the help of ropes and cables.
The maritime surveying company Martek Marine uses a drone fleet designed for turbine-blade inspections onshore or offshore. Qualified and trained pilots quickly and accurately identify and assess faults.
Traditional surveying requires turbines to be offline for two hours up to a day, but Martek’s inspection process reduces this time to 45 minutes.
Following the inspection, the client can access the data through Martek’s secure, cloud-based asset management portal where they can download a detailed PDF report and access raw survey data.
Fully Autonomous Drones? Fully autonomous drones could be the next big thing for maritime surveying. The drones can be pre-loaded with a 3D model of the ship. This allows the drone to autonomously work its way around the vessel, stopping at points of interest to obtain detailed video or image data.
Advancing this further, a drone could be designed to create its own 3D map of the vessel before carrying out the survey independently.
This article is excerpted from a blog by Martek Marine, a UK-based maritime surveying company. Read the full blog, with more details and examples.
Harxon is showcasing a series of GNSS antennas and wireless data-link modems at 2017 Intergeo, being held Sept. 26-28 in Berlin, Germany.
The products aim to provide the user better industrial solutions in the fields of surveying and mapping, precision agriculture and unmanned aerial vehicles (UAVs).
The Harxon D-Helix Antenna.
D-Helix Antenna: The multi constellation antenna is capable of superior tracking signals from 4 satellite constellations, including GPS L1/L2 L-Band, GLONASS L1/L2, BDS B1/B2/B3 and Galileo. The innovative quadrifilar helix antenna design of low wind-resistance is ideal for aerial photographs, telemetry technology, disaster monitoring and security monitoring industries. Its 3.5dBi peak gain ensures exceptional low elevation tracking performance. The low noise figure enhanced transmission interference reduction and improve the signal quality.
The Harxon GPS 1000 Survey Antenna.
Survey Antenna GPS 1000: The all constellation GNSS antenna has passed the NGS certification, which receives GPS L1/L2/L5 L-Band, BDS B1/B2/B3, GLONASS L1/L2, Galileo E1/E2/E5a/E5b signals. It can be used in land survey, marine survey, channel survey and agriculture applications, with a consistent performance across the full bandwidth. GPS 1000 has high gain and wide beam width to ensure the signal receiving performance of satellite at the low elevation angle, and the phase center remains constant as the azimuth and elevation angle of the satellites change. The influence of measurement error can be minimized via the multi-feed design and embedded multi-path rejection board.
Rover Radio HX-DU1603D: The high-speed, Bluetooth-enabled ruggedized UHF rover radio is designed for GNSS/RTK surveying and positioning. It ensures the data communication between 410MHz and 470 MHz in either 12.5KHz or 25 KHz channels. HX-DU1603D is equipped with a Bluetooth transceiver for wireless communications of external devices, features a 6800mAh rechargeable internal battery and configurable transmit power between 0.5W and 2W, also the IP67 waterproof capability allows outdoor long operational hours.
Harxon Frequency Hopping Module HX-DU1018D/HX-DU2017D.
Frequency Hopping Module HX-DU1018D/HX-DU2017D: The built-in frequency hopping transceiver modules are small size, light weight, low power consumption and strong resistance to disturbance. They provide a reliable, high speed and low latency data transmission, which are suitable for UAV flight control. These modules support a band range among 400MHz, 840MHz and 900MHz and long distance of communication. Besides, HX-DU1018D/HX-DU2017D can realize a switchover between air baud rate and serial port baud rate.
Harxon Smart Antenna.
Smart Antenna: It is a multi-functional GNSS product which is integrated by multi-frequency OEM antenna, OEM receiver and frequency hopping transceiver. Smart Antenna utilizes the dual anti-multipath antenna to receive stable GNSS signals under the bad-signal environment and precisely output the direct information with a centimeter-level positioning accuracy. The IP67 waterproof design allows the smart antenna for a long time outdoor operation.
The Harxon H-RTK.
H-RTK: H-RTK is for UAV positioning and navigation, which reaches the positioning accuracy to a centimeter level. It is integrated with positioning, height setting and heading functions to provide accurate, reliable solutions. H-RTK ensures the positioning accuracy to a centimeter level for a more stable flightpath. Also, it provides the reliable height information and solve the height-error problem to prevent air turbulence. H-RTK outputs precise navigation information with powerful magnetic disturbance resistance, it enables the flight reliability under a magnetic disturbance environment, and avoid security risks. The built-in anti-interference frequency hopping transceiver helps data transfer back to the base station, and supports the frequencies of 400 MHz, 840 MHz and 900 MHz.
For more information,visit Harxon’s booth at Intergeo in Hall 4.1 booth C4.013.
SBG Systems will demonstrate Qinertia, its in-house next-generation INS/GNSS post-processing software, at the Intergeo trade show, which takes place Sept. 26-28 in Berlin.
SBG Systems can be found is in Hall 1.1, stand C1.007.
For more than 10 years, SBG Systems has been designing inertial navigation systems from the internal inertial measurement unit (IMU) to filtering with GNSS data.
Designed for the surveying market, Qinertia is a fully in-house INS/GNSS post-processing kinematic (PPK) software. Whether the survey is made from a car, a UAV, a plane or a vessel, Qinertia will secure and enhance a surveyor’s acquisition, the company said.
The company will hold four live demonstration at its stand during Intergeo. The demonstrations will take place at 11 a.m. and 15 p.m. on both Tuesday, Sept. 26, and Wednesday, Sept. 27. There is no need to book to attend a demonstration, but please note that seats are limited.
A GPS-lidar fusion technique implements a novel method for efficiently modeling lidar-based position error covariance based on features in the point cloud. The fusion uses a three-dimensional (3D) city model to detect and eliminate non-line-of-sight (NLOS) GPS satellites to improve global positioning.
The technique has potential application in UAV missions such as 3D modeling, filming, surveying, search and rescue, and package delivery.
By Akshay Shetty and Grace Xingxin Gao, University of Illinois
Unmanned aerial vehicles (UAVs) commonly rely on GPS for continuous and accurate outdoor position estimates. However, in certain urban scenarios, additional onboard sensors such as light detection and ranging (lidar) are desirable due to errors in GPS measurements. To fuse these measurements it is important, yet challenging, to accurately characterize their error covariance. We propose a GPS-lidar fusion technique with a novel method for efficiently modeling the position error covariance based on surface and edge features in point clouds. We use the lidar point clouds in two ways: to estimate incremental motion by matching consecutive point clouds; and, to estimate global pose (position and orientation) by matching with a 3D city model. For GPS measurements, we use the 3D city model to eliminate NLOS satellites and model the measurement covariance based on the received signal-to-noise-ratio (SNR) values. Finally, all the above measurements and error covariance matrices are input to an unscented Kalman Filter (UKF), which estimates the globally referenced pose of the UAV. To validate our algorithm, we conducted UAV experiments in GPS-challenged urban environments on the University of Illinois at Urbana-Champaign campus.These experiments demonstrate a clear improvement in the UAV’s global pose estimates using the proposed sensor fusion technique.
SITUATION
Emerging applications in UAVs such as 3D modeling, filming, surveying, search and rescue, and package delivery all involve flying in urban environments. In these scenarios, autonomously navigating a UAV has certain advantages such as optimizing flight paths and sensing and avoiding collisions. However, to enable such autonomous control, we need a continuous and reliable source for UAV positioning. In most cases, GPS is primarily relied on for outdoor positioning. However, in an urban environment, GPS signals from the satellites are often blocked or reflected by surrounding structures, causing large errors in the position output.
In cases when GPS is unreliable, additional onboard sensors such as lidar can provide the navigation solution. An onboard lidar provides a real-time point cloud of the surroundings of the UAV. In a dense urban environment, lidar can detect a large number of features from surrounding structures such as buildings.
Positioning based on lidar point clouds has been demonstrated primarily by applying different simultaneous localization and mapping (SLAM) algorithms. In many cases, algorithms implement variants of iterative closest point (ICP) to register new point clouds.
APPROACH
The main contribution of this article is a GPS-lidar fusion technique with a novel method for efficiently modeling the error covariance in position measurements derived from lidar point clouds. Figure 1 shows the different components involved in the sensor fusion.
Figure 1. Overview of sensor fusion architecture.
We use the lidar point clouds in two ways: to estimate incremental motion by matching consecutive point clouds; and, to estimate global pose by matching with a 3D city model. We use ICP for matching the point clouds in both cases.
For the lidar-based position estimates, we proceed to build the error covariance model depending on the surrounding point cloud. First, we extract surface and edge feature points from the point cloud. We then model the position error covariance based on these individual feature points. Finally, we combine all the individual covariance matrices to model the overall position error covariance ellipsoid.
For the GPS measurement model, we use the pseudorange measurements from a stationary reference receiver and an onboard GPS receiver to obtain a vector of double-difference measurements. Using the double-difference measurements eliminates clock bias and atmospheric error terms, hence reducing the number of unknown variables. We use the global position estimate from the lidar-3D city matching to construct LOS vectors to all the detected satellites. We then use the 3D city model to detect NLOS satellites, and consequently refine the double-difference measurement vector. We create a covariance matrix for the GPS double-difference measurement vector based on SNR of the individual pseudorange measurements.
We implement a UKF to integrate all lidar and GPS measurements. Additionally, we incorporate orientation, orientation rate and acceleration measurements from an onboard inertial measurement unit (IMU). Finally, we test the filter on an urban dataset to show an improvement in the navigation solution.
LIDAR-BASED ODOMETRY
ICP is commonly used for registering three-dimensional point clouds. It takes a reference point cloud q, an input point cloud p, and estimates the rotation matrix R and the translation vector T between the two point clouds. Different variants of the algorithm generally consist of three primary steps.
Matching. This involves matching each point pi in the input point cloud, to a point qi in the reference point cloud. The most common method is to find the nearest neighbors of each point in the input point cloud. For our application, a kDtree performs best since the two point clouds are relatively close to each other.
Defining Error Metric. This defines the error metric for the point pairs. We choose the point-to-point metric, which is generally more robust to difficult geometry than other metrics such as point-to-plane. The total error between the two point clouds is defined as follows:
(1)
where N is the number of points in the input point cloud p.
Minimization. The last step of the algorithm is the minimization of the error metric with regard to the rotation matrix R and the translation vector T between the two point clouds.
We use ICP to estimate the incremental motion of the lidar between consecutive point clouds. Figure 2 shows our implementation of ICP to estimate the lidar odometry.
Figure 2. The input to ICP is a reference point cloud q and an input point cloud p as shown in (a). The algorithm calculates the rotation matrix R and the translation vector T such that the error metric E is minimized. (b) shows the reference point cloud q and the transformed input point cloud R • p + T.
MATCHING LIDAR, 3D MODEL
We generate our 3D city model using data from two sources: Illinois Geospatial Data Clearinghouse and OSM. The Illinois Geospatial Data were collected by a fixed-wing aircraft flying at an altitude of 1700 meters, equipped with a lidar system including a differential GPS unit and an inertial measurement system to provide superior global accuracy. Since the data were collected from a relatively high altitude, it primarily contains adequate details for the ground surface and the building rooftops. In order to complete the 3D city model, we need additional information for the sides of buildings. We use OSM to obtain this information. OSM is a freely available, crowd-sourced map of the world, which allows users to obtain information such as building footprints and heights. Figure 3 shows a section of the 3D city model for Champaign County.
Figure 3. Section of the point cloud for Champaign County dataset. (Left) shows the 3D city model using only the Illinois Geospatial Data. (Right) fhows the model after incorporating building information from OpenStreetMap.
To estimate the global pose of the lidar, we match the onboard lidar point cloud with the 3D city model using ICP, in these steps:
Use the position output from onboard GPS receiver as an initial guess. If position output is unavailable, use the position estimate from the previous iteration as an initial guess. For orientation, use the estimate from the previous iteration. Thus, we obtain an initial pose guess .
Project the onboard lidar point cloud pL to the same space as the 3D city model qcity using .
Implement ICP, to obtain the rotation RL and translation TL between the two point clouds. Use this output to obtain an estimate for the global pose .
Figure 4 shows the results of implementation of the above method. While navigating in urban areas, the GPS receiver position output used for the initial pose guess might contain large errors in certain directions. This might cause ICP to converge to a local minimum, depending on features in the point cloud pL generated by the onboard lidar.
Figure 4. Global pose estimation with the aid of 3D city model. (Left) shows the intial position guess (red dot, with term in red outlined box) and the onboard lidar point cloud pL projected on the same space as the 3D city model qcity. (b) shows the updated global position (green dot, with term in green outlined box) after the ICP step. We see an improvement in the global position, as the point cloud matches with the 3D city model.
To evaluate how our lidar-3D city model matching algorithm performs in such challenging cases, we test it in two different urban areas as shown in Figure 5. We begin by selecting a grid of initial position guesses up to 20 meters away from the true position. With an adequate distribution of features, ICP is able to correctly match the two point clouds and provide an accurate position estimate after matching. In contrast, when there’s an urban scenario with a relatively poor distribution of features, ICP is unable to estimate the position accurately.
Figure 5. Lidar-3D city model matching in two different urban areas. We begin with a grid on initial position guesses (red) around the true position (black). In (a) and (b), there are adequate features. The position estimates after matching (blue) converge to the true position. In (c) and (d) the feature distribution is relatively poor. The position estimates after matching (blue) are parallel to the building surface.
MODELING ERROR COVARIANCE
We model the lidar position error covariance as a function of the surrounding features. In urban environments, we typically observe structured objects such as buildings, hence we focus primarily on surface and edge features in the point cloud. We extract these feature points based on the curvature at each point. Points with curvature values above a threshold are marked as edge points, whereas points with curvature values below a threshold are marked as surface points. (For detailed discussion of the algorithms involved, see GPS-LiDAR_AkshayShetty-algorithms.
For each surface feature point, we first compute the normal by using 9 of the neighboring points to fit a plane. We model the error covariance ellipsoid with the hypothesis that each surface feature point contributes in reducing position error in the direction of the corresponding surface normal. Additionally, we assume that surface points closer to the lidar are more reliable than those further away, because of the density of points.
For each edge feature point, we first find the direction of the edge using the closest edge points in the scans above and below. We model the error covariance ellipsoid with the hypothesis that each edge feature point helps in reducing position error in the directions perpendicular to the edge vector. A vertical edge, for example, would help in reducing horizontal position error. Additionally, we assume that edge points closer to the lidar are more reliable than those further away, again because of the density of points. Figure 6 shows sample error covariance ellipsoids for a surface point and an edge point.
Figure 6. Position error covariance ellipsoid for surface and edge feature points. The exact sizes of the ellipsoids are tuned during implementation.
To obtain the overall position error covariance, we combine the error covariance matrices for all the individual surface and edge feature points. Figure 7 shows the combined covariance ellipsoid for two different scenarios. We observe that while passing through a corridor, the covariance ellipsoid is larger in the direction parallel to the building sides due to a poor distribution of features.
Figure 7. Overall position error covariance ellipsoids (black) for two point clouds (green). We combine the error ellipsoids from individual surface (red) and edge (blue) feature points.
GPS MEASUREMENT MODEL
We use pseudorange measurements from the GPS receiver to create the measurement model. To eliminate certain error terms, we use double-difference pseudorange measurements, which are calculated by differencing the pseudorange measurements between two satellites and between two receivers. Before proceeding to use the pseudorange measurements, we check if any of the satellites detected by the receiver are NLOS signals. We use the 3D city model mentioned earlier to detect the NLOS satellites. We use the position output generated by the lidar-3D city model matching to locate the receiver on the 3D city model.
Next, we draw LOS vectors from the receiver to every satellite detected by the receiver and eliminate satellites whose corresponding LOS vectors intersect the 3D city model. Figure 8 shows the above implementation in an urban scenario.
Figure 8. Elimination of NLOS satellite signals. LOS vectors are drawn to all detected satellites: SV3, SV14, SV16, SV22, SV23, SV26, SV31. The LOS vectors to satellites SV23 and SV31 intersect (red) the 3D city model and are eliminated from further calculations.
After eliminating the NLOS satellites, we select satellites that are visible to both the user and the reference receivers to create the GPS double-difference measurement vector and its covariance. We assume that the individual pseudorange measurements are independent, and that the variance for each measurement is a function of the corresponding SNR. We propagate the covariance matrix for the individual pseudorange measurements, to obtain the covariance matrix for the double-difference measurements.
GPS-Lidar Integration
In addition to using a lidar and a GPS receiver, we use an IMU on board the UAV. Figure 9 shows the experimental setup: the UAV designed and built by our research group. For the double-difference GPS measurements, we use a reference receiver within a kilometer of our data collection sites. We implement a UKF to fuse measurements from the sensors and estimate the global pose of the UAV.
Figure 9. Experimental setup for data collection. Our custom-made iBQR UAV mounted with a lidar, a GPS receiver and antennas, an IMU, and an onboard computer.
Position and orientation estimates from lidar and GPS are incorporated via the correction step of the filter, whereas the IMU measurements are included in the prediction step. For position corrections from lidar, we use our point cloud feature based model for the error covariance. For GPS double-difference measurements, we use the covariance based on the individual pseudorange measurement SNR.
We implement our algorithm on an urban dataset collected on our campus of University of Illinois at Urbana-Champaign. As shown in Figure 10, the GPS measurements and the GPS position output contain large errors, due to the presence of nearby urban structures. Here we stack all the double-difference measurements and compute the unweighted least square estimate of the baseline between the UAV and the reference receiver.
Figure 10. Position estimates from GPS measurements. The position output from the GPS receiver (blue) and the unweighted least-squares position estimate (red) contain large errors.
For the lidar measurements, we check the output from our incremental ICP odometry method and the lidar-3D city model matching algorithm. Furthermore, we implement an ICP mapping algorithm to check the performance of existing ICP-based methods on the dataset. In Figure 11, the ICP odometry method and the ICP mapping algorithm accumulate drift over the course of the trajectory. The lidar-3D city model matching algorithm does not drift over time; however, the position still contains errors in situations where the lidar does not detect enough number of points or the matching algorithm converges to a local minimum.
Figure 11. Position estimates from lidar point clouds. The incremental ICP odometry (green) and the ICP mapping (blue) estimates accumulate drift over time. The lidar-3D city model matching (yellow) does not drift over time, but contains errors where the ICP algorithm might converge to a local minimum.
Figure 12 shows the output of the filter for the same trajectory. The filter output estimates the actual path much more accurately than the individual measurement sources by themselves.
Figure 12. Position estimates from UKF, integrating GPS and lidar measurements. The filter position output (blue) resembles the actual trajectory, more accurately than any individual source of GPS or lidar measurements.
CONCLUSION
In summary, we proposed a GPS-lidar integration approach for estimating the navigation solution of UAVs in urban environments. We used the onboard lidar point clouds in two ways: to estimate the odometry by matching consecutive point clouds, and to estimate the global pose by matching with an external 3D city model. We built a model for the error covariance of the lidar-based position estimates as a function of surface and edge feature points in the point cloud. For GPS measurements, we eliminated NLOS satellites using the 3D city model and used the remaining double-difference measurements between an onboard receiver and a reference receiver. To construct the covariance matrix for the double-difference measurements, we used the SNR values for individual pseudorange measurements.
Finally, we applied an UKF to integrate the measurements from lidar, GPS and an IMU. We experimentally demonstrated the improved positioning accuracy of our filter.
ACKNOWLEDGMENTS
The authors would like to sincerely thank Kalmanje Krishnakumar and his group at NASA Ames Research Center for supporting this work under the grant NNX17AC13G.
The material in this article was first presented at the ION GNSS+ 2017 conference in Portland, September 2017.
MANUFACTURERS
The lidar used aboard the UAV in these tests is a Velodyne VLP-16 Puck Lite. The GPS receiver is a u-blox LEA-6T with a Maxtena M1227HCT-A2-SMA antenna. The IMU is an Xsens Mti-30, and the onboard computer an AscTec Mastermind 3a.
The iBQR UAV was designed and assembled by the authors.
AKSHAY SHETTY received an M.S. degree in aerospace engineering from University of Illinois at Urbana-Champaign. He is also pursuing a Ph.D. at the same university.
GRACE XINGXIN GAO received a Ph.D. degree in electrical engineering from Stanford University. She is an assistant professor in the Aerospace Engineering Department at the University of Illinois at Urbana-Champaign.
Even a GNSS receiver that can supply raw pseudorange and carrier-phase measurements now costs only a few hundred dollars, and in this month’s column, a couple of researchers from Down Under pit a couple of these receivers up against a couple of survey-grade receivers. Did this cheap receiver turn out to be a good thing?
By Robert Odolinski and Peter J.G. Teunissen
ALL GOOD THINGS ARE CHEAP; ALL BAD ARE VERY DEAR. That’s what the famous American essayist (and surveyor) Henry David Thoreau wrote in his diary on March 3, 1841. He was likely referring, in part, to the cheapness of the things he came across in nature such as birdsong or the plants and trees on the shores of Walden Pond and the dearness of some luxuries and comforts of civilization, which he tended to eschew. But what has that got to do with GPS, you might ask?
When they were first introduced in the late 1970s and early 1980s, GPS receivers were very dear. Many of them sold for anywhere from $50,000 to $250,000, which would be equivalent to about twice those amounts in today’s dollars. The first civilian receivers were large bulky affairs. As I documented in this column in April 1990 (“Smaller and Smaller: The Evolution of the GPS Receiver”), the “first commercially available GPS receiver was the STI-5010 built by Stanford Telecommunications Inc. It was a dual-frequency, C/A- and P-code, slow-sequencing receiver. Cycling through four satellites took about five minutes, and the receiver unit alone required about 30 centimeters of rack space. External counters, also requiring rack space, made pseudorange measurements. An external computer controlled the receiver and computed positions.” While it could be transported in a small truck (and some were), it was not designed for portability and ease of use by surveyors or geodesists.
Then, in 1982, Texas Instruments introduced the first relatively compact civil GPS receiver, the TI 4100, also known as the Navstar Navigator. And as I also noted in that column more than 15 years ago, this “receiver could make both C/A- and P-code measurements along with carrier-phase measurements on both L1 and L2 frequencies. Its single hardware channel could track four satellites simultaneously through a multiplexing arrangement. The 37 × 45 × 21-centimeter receiver/processor had a handheld control and display unit and an optional dual-cassette data recorder for saving measurements for post-processing. The unit, although portable, weighed 25 kilograms and consumed 110 watts of power (the receiver doubled as a hand warmer). Field operation required a supply of automobile batteries.”
My, how things have changed. Beginning around 1990, receivers steadily got smaller and smaller and cheaper and cheaper. Survey-grade GNSS (not just GPS) receivers can now be purchased for well under $10,000 and consumer-grade units sell for as little as a hundred dollars or less. And, of course, the GNSS modules inside smartphones and other devices cost manufacturers only a couple of dollars or so.
But even a GNSS receiver that can supply raw pseudorange and carrier-phase measurements now costs only a few hundred dollars, and in this month’s column, a couple of researchers from Down Under pit a couple of these receivers up against a couple of survey-grade receivers. Did this cheap receiver turn out to be a good thing?
Read on to find out.
GPS has been the number-one positioning tool for a range of applications during the past few decades. The integration of the emerging global navigation satellite systems, such as the Chinese BeiDou Navigation Satellite System (BDS), can give improved precise (millimeter- to centimeter-level) real-time kinematic (RTK) positioning. When BDS is combined with GPS, about double the number of satellites are visible in the Asia-Pacific region, which can make single-frequency RTK and low-cost receiver RTK positioning possible.
In this article, we will analyze the performance of L1 GPS + B1 BDS in Dunedin, New Zealand, using low-cost receivers. We compare their performance to that of L1+L2 GPS survey-grade receivers.
First, we describe the GPS+BDS functional and stochastic models and the data used for our evaluations. Least-squares variance component estimation (LS-VCE) is used as a means to determine the code and phase (co)variances to formulate a realistic stochastic model. (An incorrect stochastic model will deteriorate the ambiguity resolution and consequently the achievable positioning precisions.)
Having correctly defined the stochastic model, we focus on the positioning performance. We investigated the ambiguity resolution and positioning performance, both formally and empirically, for customary and high-elevation cut-off angles. The high cut-off angles are used to mimic situations when low-elevation multipath is to be avoided. Lastly, we compared all our results between using low-cost and survey-grade antennas.
GPS+BDS POSITIONING MODEL
The model that we used for positioning is given as follows. Assume that sG + 1 GPS satellites are tracked on fG frequencies and sB + 1 BDS satellites on fB frequencies. As we apply system-specific double-differencing (DD), one pivot satellite is used per system. The total number of DD phase and code observations per epoch then equals 2 fGsG + 2 fBsB. We assume for now that cross-correlation between frequencies as well as code and phase is absent. The combined multi-frequency short-baseline GPS+BDS model is then defined as follows.
The system-specific DD phase and code observation vectors are denoted as φ* and p*, respectively, with * = {G, B} where G = GPS and B = BDS. The single-epoch GNSS model of the combined system is given as
(1)
and
(2)
in which
is the combined phase vector,
is the combined code vector,
is the combined integer ambiguity vector,
is the real-valued baseline vector,
is the combined phase random observation noise vector,
is the combined code random observation noise vector, and
D[.] denotes the dispersion operator.
The entries of the baseline design and wavelength matrices are given as
where is the x 1 vector of 1s, is the differencing matrix, is the unit matrix, the geometry-matrices GG and GB contain the undifferenced receiver-satellite unit direction vectors for GPS and BDS, respectively, is the wavelength of frequency , denotes the Kronecker product, and “diag” and “blkdiag” indicate diagonal and block diagonal matrices, respectively. The entries of the positive definite variance matrices are given as
(3)
where , denote the phase and code standard deviation, respectively, and the satellite elevation-angle-dependent weight.
The model in Equation 1 applies to short baselines, and thus the ionospheric and tropospheric delays are assumed absent. The broadcast ephemerides are used to obtain the satellite coordinates. Further, the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) technique is used to estimate the integer ambiguities a. The observation noise vectors εand e, respectively, are zero-mean vectors, provided that no multipath is present in Equation 1.
EXPERIMENT SETUP
The GNSS receivers we used are depicted in FIGURE 1. Firstly, two low-cost single-frequency receivers were set up to collect L1+B1 GPS+BDS data for two days. These receivers cost a few hundred U.S. dollars. Since the patch antennas we used have been shown to have less effective signal reception and multipath suppression in comparison to survey-grade antennas, the receivers that collected data for two days were additionally connected to such antennas. These antennas have a cost of slightly more than US$1,000 per antenna. To compare the low-cost solution to a survey-grade receiver-solution, two such receivers (which cost several thousand U.S. dollars) were connected to the same survey-grade antennas through splitters and collected L1+L2 GPS data. A detection, identification and adaption procedure was used to eliminate any outliers.
FIGURE 1. Low-cost single-frequency receivers collecting GPS+BDS data for single-baseline RTK, with patch antennas (left) and survey-grade antennas (right) on Jan. 4–6 and Jan. 6–8, 2016, respectively. Survey-grade dual- frequency GPS receivers were connected to the same survey-grade antennas simultaneously to truly track the same GPS constellation.
FIGURE 2 depicts the corresponding redundancy of the two receiver models (that is, the number of observations minus the number of estimated unknowns) together with the number of satellites over 48 hours (30-second epoch interval). The number of BDS satellites (magenta lines) is overall smaller than when compared to GPS (blue lines) in Dunedin. However, Figure 2 also shows that the model strength of L1+B1 GPS+BDS, as measured by its redundancy, is almost similar to that of L1+L2 GPS except for some hours at the middle of the two days. This implies that the two receiver models can potentially give competitive RTK ambiguity resolution and positioning performance. This is however only true if the receiver code and phase observation noise would be of similar magnitude between the receivers used, hence the need for an analysis of the receiver observation precision.
FIGURE 2. Redundancy (left) and number of satellites (right) of L1+B1 GPS+BDS and L1+L2 GPS during Jan. 6–8, 2016, (48 hours) for an elevation cut-off angle of 10°.
In our receiver evaluations, we determined a set of reference ambiguities by using a known baseline and treating them as time-constant parameters over the two days in a dynamic model.
LOW-COST RTK POSITIONING
The code and phase variances were estimated by LS-VCE using data independent from the data used for the following positioning analysis. The variances are needed to formulate a realistic stochastic model, whereas an incorrect stochastic model will deteriorate the ambiguity resolution and consequently the achievable positioning precisions. TABLE 1 depicts the corresponding estimated standard deviations (STDs) used for our positioning models.
TAB LE 1. Zenith-referenced undifferenced code and phase standard deviations estimated by least-squares variance component estimation.
Table 1 shows that the code precision of L1 GPS and B1 BDS improves significantly when the survey-grade antennas are used instead of patch antennas (49 centimeters STD for L1/B1 that decreases to about 30 centimeters), due to their better signal reception and multipath suppression abilities. For testing our stochastic model, we used data that is independent from the data used to estimate the code/phase precision.
Positioning Performance. The single-epoch (instantaneous) RTK positioning results for 24 hours data are shown in FIGURE 3, with ambiguity-float solutions shown at the top and ambiguity-fixed solutions at the bottom. Only the correctly fixed solutions are depicted as determined by comparing the instantaneously estimated ambiguities to the set of reference ambiguities. The 95% empirical and formal confidence ellipses and intervals are shown in green and red, respectively. They were computed from the empirical and formal position variance matrices. The empirical variance matrix was estimated from the positioning errors as obtained from comparing the estimated positions to precise benchmark coordinates. The formal variance matrix used was determined from the mean of all single-epoch formal variance matrices.
FIGURE 3. Horizontal (north (N), east (E)) position scatter and corresponding vertical (U) time series of the float (top) and correctly fixed (bottom) L1+B1 GPS+BDS single-epoch RTK solutions for an elevation cut-off angle of 10°. The 95% empirical and formal confidence ellipses and intervals are shown in green and red, respectively. The 24 hour (30 second) period is 22:00-22:00 UTC Jan. 5-6, 2016, for patch antennas in (a) and 21:48-21:48 UTC Jan. 8-9, 2016, for survey-grade antennas in (b), which are periods independent of the periods used to determine the stochastic model through the code/phase STDs in Table 1.
Figure 3 shows a good fit between the formal and empirical confidence ellipses/intervals, which thus illustrates realistic LS-VCE STDs in Table 1 that were used in the stochastic model. Note also the two-order of magnitude improvement when going from float to fixed solutions, and that the low-cost receiver plus survey-grade antenna has the most precise ambiguity-float positioning solutions.
Ambiguity Resolution and Positioning Performance for Higher Cut-Off Angles. We subsequently investigated the low-cost L1+B1 GPS+BDS performance for high elevation cut-off angles, so as to mimic situations in urban canyon environments or when low-elevation-angle multipath is present and is to be avoided. We have made comparisons to the survey-grade L1+L2 GPS results. It has been shown that a good ambiguity resolution performance does not necessarily imply a good positioning performance, so we investigated what effect this has on our positioning models.
The following integer least-squares (ILS) success rates (SRs) are thus computed based on epochs with the condition of positional dilution of precision (PDOP) ≤ 10 and averaged over all epochs over two days of data. By including and excluding epochs with large PDOPs, we can show how the positioning performance of the different models is affected by poor receiver-satellite geometries. To better understand how this exclusion of epochs with large PDOPs also influenced the empirical ambiguity-correctly-fixed positioning performance, we constructed TABLE 2, which shows the corresponding positioning STDs for two days of data. These STDs were computed by comparing the estimated positions to precise benchmark coordinates. In addition to the positioning performance, we depict in Table 2 the corresponding empirical ILS SR for full ambiguity-resolution, which is given by the ratio of the number of correctly fixed epochs to the total number of epochs.
TABLE 2. Single-epoch empirical STDs (N, E, U) of correctly fixed positions for the three positioning models together with their ILS SR for four elevation cut-off angles and 48 hours of data (Jan. 4–6 and Jan. 6–8, 2016). The empirical STDs and ILS SRs are also shown when conditioned on PDOP ≤ 10.
Table 2 shows that the L1+B1 low-cost receiver plus patch antenna combination has (as expected) smaller SRs in comparison to those when the survey-grade antenna is used. This latter combination has comparable SRs to the (PDOP-conditioned) SRs of the survey-grade L1+L2 GPS receiver for cut-off angles up to 25°.
In support of better understanding Table 2, FIGURE 4 shows typical positioning results for the different receiver and antenna combinations with elevation cut-off angles of 10° (top two rows) and 25° (bottom two rows). The first and third rows show the local horizontal (N, E) positioning scatterplots and the second and fourth rows the vertical (U) time series over two days of data. The float solutions are depicted in gray, and incorrectly and correctly fixed solutions in red and green, respectively. The zoom-in is given to better show the spread of the correctly fixed solutions with millimeter-centimeter level precisions. The formal ambiguity-float STDs are also shown under the up time series to reflect consistency between the empirical and formal positioning results.
FIGURE 4. Horizontal (N, E) scatterplots and vertical (U) time series for L1+B1 low-cost receiver with patch antenna (first column) with 99.5% (89.8%) ILS SR, L1+B1 low-cost receiver with survey-grade antenna (second column) with 100% (97.8%) ILS SR, and survey-grade L1+L2 GPS (third column) with 100% (94.1%) ILS SR, using 10° (top two rows) and 25° (bottom two rows) cut-off angles respectively (Jan. 4–6, 2016, for low-cost receiver with patch antenna and Jan. 7–8, 2016, for the low-cost and survey-grade receivers with survey-grade antennas). The SRs are conditioned on PDOP ≤ 10 and computed based on all epochs. Below the vertical time series, the ADOP is depicted in blue color, the 0.12-cycles level as red, and ambiguity-float vertical formal STDs are shown in gray.
We also depict in Figure 4 the ambiguity dilution of precision (ADOP) as an easy-to-compute scalar diagnostic to measure the intrinsic model strength for successful ambiguity resolution. The ADOP is defined as
(cycles) (4)
with n being the dimension of the ambiguity vector, the ambiguity variance matrix, and |.| denoting the determinant. ADOP gives a good approximation to the average precision of the ambiguities, and it also provides for a good approximation to the ILS SR. The rule-of-thumb is that an ADOP smaller than about 0.12 cycles corresponds to an ambiguity SR larger than 99.9%.
Figure 4 shows that more solutions are incorrectly fixed (red dots) when the ADOPs (blue lines) are larger than the 0.12 cycle level (red dashed lines). The figure also reveals that the L1+B1 low-cost receiver plus patch antenna combination achieves an ILS SR (99.5%) similar to that of the survey-grade L1+L2 GPS receiver (SR of 100%) for the cut-off angle of 10°. This ILS SR corresponds to the availability of correctly fixed solutions (green dots) with millimeter-centimeter level positioning precision over the two days. The L1+L2 GPS receiver has, moreover, large ambiguity-fixed positioning excursions at the same time as the formal STDs are large for the cut-off angle of 25° due the poor GPS-only receiver-satellite geometry for this high cut-off angle. This is also reflected by the corresponding relatively large ambiguity-fixed STDs depicted in Table 2 that are improved from decimeter- to millimeter-level when the PDOP ≤ 10 condition is applied. Figure 4 also shows that the L1+B1 low-cost receiver with the survey-grade antenna has a larger SR of 97.8% when compared to the PDOP-conditioned SR for L1+L2 GPS of 94.1% for the cut-off angle of 25° (see also Table 2), owing to the use of BDS that significantly improves the receiver-satellite geometry.
Finally, we also tested the low-cost receiver-solution (with survey-grade antennas) for a baseline length of 7 kilometers, where (small) residual slant ionospheric delays are present. It was shown that this combination still has the potential to achieve ambiguity resolution and positioning performance competitive with the survey-grade receiver-solution.
CONCLUSIONS
In this article, we evaluated a low-cost L1+B1 GPS+BDS RTK setup and compared its ambiguity resolution and positioning performance to a survey-grade L1+L2 GPS solution in Dunedin, New Zealand. The LS-VCE procedure was used to determine the variances of the low-cost receivers. The estimated variances are needed so as to formulate a realistic stochastic model, otherwise the ambiguity resolution and hence the achievable positioning precisions would deteriorate.
Since we analyzed a short baseline, the LS-VCE variances were shown to likely be affected by multipath. To mitigate multipath we connected the low-cost receivers to survey-grade antennas with better signal reception and multipath suppression abilities. It was shown that the survey-grade antennas can significantly improve the performance for the low-cost receivers so that the code/phase noise estimates more resemble that of survey-grade receivers. The LS-VCE STDs were furthermore shown to be realistically estimated for an independent time period.
We also demonstrated that the low-cost receivers can give competitive instantaneous ambiguity resolution and positioning performance to that of the survey-grade receivers. This is particularly true when the low-cost receivers are connected to survey-grade antennas.
ACKNOWLEDGMENTS
This article is based on the paper “On the Performance of a Low-cost Single-frequency GPS+BDS RTK Positioning Model” presented at the 2017 International Technical Meeting of The Institute of Navigation held Jan. 30-Feb. 1, 2017, in Monterey, California.
Ryan Cambridge at the School of Surveying, University of Otago, collected the low-cost receiver data. Author Peter J.G. Teunissen was supported by an Australian Research Council Federation Fellowship. All of this support is gratefully acknowledged.
MANUFACTURERS
The low-cost receivers used in the research were u-blox EVK-M8T receivers. The survey-grade receivers were Trimble NetRS receivers. The patch antennas were u-blox ANN-MS antennas, while the survey-grade antennas were Trimble Zephyr 2 GNSS antennas.
ROBERT ODOLINSKI conducted his Ph.D. studies at Curtin University, Perth, Australia, from 2011 to 2014. His research focus is next-generation multi-GNSS integer ambiguity resolution enabled precise positioning. In 2015, Odolinski started his position as a lecturer/research fellow in geodesy/GNSS at the School of Surveying, University of Otago, New Zealand.
PETER J.G. TEUNISSEN is a professor of geodesy and navigation and the head of the Curtin GNSS Research Centre, Curtin University. He is also with the Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands. His research interests include multiple GNSS and the modeling of next-generation GNSS for high-precision positioning, navigation and timing applications.
FURTHER READING
• Authors’ Conference Paper
“On the Performance of a Low-cost Single-frequency GPS+BDS RTK Positioning Model” by R. Odolinski and P.J.G. Teunissen in Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, Jan. 30 – 1 Feb., 2017, pp. 745–753.
• Authors’ Related Work
“Single-Frequency, Dual-GNSS Versus Dual-frequency, Single-GNSS: A Low-cost and High-grade Receivers GPS-BDS RTK Analysis” by R. Odolinski and P.J.G. Teunissen in Journal of Geodesy, Vol. 90, No. 11, 2016, pp. 1255–1278, doi:10.1007/s00190-016-0921-x.
“Combined BDS, Galileo, QZSS and GPS Single-frequency RTK” by R. Odolinski, P.J.G. Teunissen and D. Odijk in GPS Solutions, Vol. 19, No. 1, 2015, pp. 151–163, doi:10.1007/s10291-014-0376-6.
“Instantaneous BeiDou+GPS RTK Positioning With High Cut-off Elevation Angles” by P.J.G. Teunissen, R. Odolinski and D. Odijk in Journal of Geodesy, Vol. 88, No. 4, 2014, pp. 335–350, doi: 10.1007/s00190-013-0686-4.
“The Future of Single-Frequency Integer Ambiguity Resolution” by S. Verhagen, P.J.G. Teunissen and D. Odijk in Proceedings of the VII Hotine-Marussi Symposium on Mathematical Geodesy, Rome, June 6–10, 2009, edited by N. Sneeuw, P. Novák, M. Crespi and F. Sanso, International Association of Geodesy Symposia, Vol. 137, 2012, pp. 33–38, doi:10.1007/978-3-642-22078-4 5.
“Centimeter-Level Positioning for UAVs and Other Mass-Market Applications” by C. Mongredien, J.-P. Doyen, M. Strom and D. Ammann in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 1441–1454.
“Initial Assessment of the COMPASS/BeiDou-2 Regional Navigation Satellite System” by O. Montenbruck, A. Hauschild, P. Steigenberger, U. Hugentobler, P.J.G. Teunissen and S. Nakamura in GPS Solutions, Vol. 17, No. 2, 2013, pp. 211–222, doi:10.1007/s10291-012-0272-x.
• LAMBDA
“On the Reliability of Integer Ambiguity Resolution” by S. Verhagen in Navigation, Vol. 52, No. 2, Summer 2005, pp. 99–110, doi: 10.1002/j.2161-4296.2005.tb01736.x.
“ADOP in Closed Form for a Hierarchy of Multi-frequency Single-baseline GNSS Models” by D. Odijk and P.J.G. Teunissen in Journal of Geodesy, Vol. 82, 2008, pp. 473–492, doi: 10.1007/s00190-007-0197-2.
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