Septentrio has launched the Mosaic high-precision GNSS receiver module.
Despite its compact size (31 x 31 x 4 millimeters, 1.29 x 1.29 x 0.15 inches), the Mosaic module supports more than 30 signals from all six GNSS constellations, L-band and various satellite-based augmentation systems, the company said.
As a multi-band module tracking all GNSS satellites in view, it is also designed to support future GNSS signals.
It also supports correction services, and uses real-time kinematic (RTK) technology, together with Septentrio’s algorithms, to guarantee maximum accuracy and availability. The surface-mount design of Mosaic is optimized for automated assembly and ease of integration, with a full library of well-documented and flexible interfaces.
“Our new Mosaic module represents the best-in-class option for reliable and scalable position accuracy, with integrity,” said Chris Lowet, product manager at Septentrio. According to Lowet, it provides RTK positioning with a power consumption of 0.6-1 W, and requires no or minimal additional components for the design-in. “These characteristics make it an ideal positioning cornerstone for a variety of mass market UAV, autonomous and robotics applications,” Lowet said.
Photo: Septentrio
Robustness to interference. Due to the natural weaknesses of distant GNSS signals and a crowded radio-frequency spectrum, GNSS-based services are vulnerable to unintentional radio-frequency interference (RFI). They are also vulnerable to intentional RFI, attacks intended to disrupt receivers by means of counterfeit GNSS-like signals (known as spoofing), and to intentional transmission of RF energy to mask GNSS signals with noise (known as jamming).
To defend against these threats, Mosaic features Septentrio’s AIM+ technology. AIM+ can suppress the widest variety of interferers, from simple continuous narrowband signals to complex wideband and pulsed jammers, the company added. In addition, the integrated spectrum analyzer allows the RF environment around any Mosaic module to be viewed in real time in both time and frequency domains.
Effective interference countermeasures against threats to GNSS signals also require constant knowledge of the changing RF environment. The Mosaic module helps analyze these threats by continuously and automatically monitoring the GNSS frequency spectrum to detect, characterize, log and mitigate interference events when needed.
Ag Leader has unveiled new guidance and steering solutions for precision agriculture, including a dual-antenna automated steering system and the latest in GNSS technology.
The GPS 7500 GNSS smart antenna. (Photo: Ag Leader)
The GPS 7500 is a field-ready, multi-frequency GNSS smart antenna providing the latest technology including access to multiple GNSS signals for up to sub-inch accuracy and increased performance in variable terrain.
When combined with NovAtel’s TerraStar-C PRO differential correction service, GPS 7500 receives multi-constellation support for better satellite availability.
A full range of performance accuracies are available from GLIDE to RTK, offering a variety of solutions for customers. Combined with SteerCommand, the GPS 7500 offers sub-inch real-time kinematic (RTK) accuracy using the Relay 400, Relay 900 or InCommand NTRIP Client.
The receivers with InCommand software. (Photo: Ag Leader)
Wi-Fi capability within GPS 7500 allows for base-station configuration from a smartphone or tablet.When uptime is valued over absolute accuracy, integrated StableLoc technology utilizes available correction signals to provide a seamless transition between correction sources — without position jumps — and maximize signal uptime.
“SteerCommand with DualTrac brings a dual-antenna offering to the market that provides RTK accuracy and meets the needs of many farmers requiring high-accuracy automated steering at low speeds,” said Bill Cran, Ag Leader product specialist. “New GNSS technology in the GPS 7500 was leveraged to make this possible and also adds new satellite and correction offerings including TerraStar-C PRO.”
The GPS 7500 supports the new TerraStar-C PRO service, available in 3-month and 12-month subscriptions. TerraStar-C PRO offers multi-constellation/multi-frequency positioning with greater accuracy, availability and reliability than before. Its convergence is 60-percent faster and accuracy 40-percent better than TerraStar-C to overcome challenging signal conditions such as multipath, shading, interference and scintillation.
SteerCommand with DualTrac. Combining the GPS 7500 receivers with SteerCommand and InCommand displays offers automated steering control with sub-inch accuracy at ultra-low speed (as low as 0.05 mph). SteerCommand with DualTrac is designed for operations requiring precise steering such as planting or harvesting bedded crops, installing drip tape or planting and harvesting specialty crops. It provides a stable heading, even when the vehicle is not moving, as well as rapid line acquisition in forward or reverse.
Agriculture equipment maker Case IH is enhancing the robustness of its RTK+ correction signal network by adding the European Galileo system to the compatible satellites with which it works.
The move will increase levels of signal reception and reliability for farmers using Case IH RTK+-guided autosteering and related technologies.
Real-time kinematic (RTK) systems typically depend on signals from the American GPS or Russian GLONASS satellite networks, both designed primarily for non-civilian use. To give European Case IH users a reliable alternative when using RTK+-guided steering systems with their sub-1.5-centimeter repeatable accuracy, Case IH AFS RTK+ now also uses Galileo.
The addition of Galileo to the global GNSS constellation helps minimize the risk of signal failure, a key driver for the integration of its signals into the Case IH AFS RTK+ signal system. European satellite network independence is a principal objective, but Case IH AFS RTK+ is also designed to be compatible with existing and planned GNSS satellites and interoperable with GPS and GLONASS.
Galileo benefits farmers by minimizing downtime from waiting for lost signals to be regained, and ensures consistent efficient use of seed, fertilizer and crop protection products through parallel passes with minimal overlap, thereby maximizing crop potential.
“The use of GNSS technology is opening up new productivity levels and opportunities in European agriculture, providing farmers with an unprecedented level of knowledge about their crops, livestock and operations while making the sector more efficient, economically competitive and environmentally sustainable,” said Maxime Rocaboy, product marketing manager, AFS technology, at Case IH.
“Enhanced RTK+ accuracy through incorporation of signals from the Galileo satellite system is a core way in which we can help Case IH tractor and combine users be innovative and competitive as they seek to help develop a sustainable agriculture to feed an ever-increasing world population in an environmentally responsible way,” Rocaboy said.
Regular readers of GPS World are aware of many of the rapidly developing technologies and navigational systems being created around the world, but often the everyday surveyor shows up late to the party.
While smartphones get the most mainstream media coverage, other navigational devices and measurement systems are adapting to evolving technical breakthroughs and new methods of transmitting a variety of data wirelessly.
This month’s article looks at the increase in satellite navigation networks along with the rollout of 5G cellular technology. Both advancements will benefit the surveying community; to start, I’ll explain what this means for accuracy and precision of survey measurements as well as productivity.
Everybody gets a constellation! (with apologies to Oprah)
I’ve been known to wax poetic in this column about my admiration of GNSS technology, and I continue to marvel at the “accidental” civilian use of a military tool. This method of measurement and navigation continues to expand, refine and transcend everyday life, and surveying is no exception.
The satellite constellation is the mainstay of this navigational system. The United States began the charge several decades ago, but other nations are quickly catching up. Let’s look at the current constellations and their status.
Operational Systems
GPS (United States)
GLONASS (Russia)
Galileo (European Union)
Beidou (China)
QZSS (Japan)
IRNSS (India)
Chart: GPS World
There are now more satellites. What’s the big deal?
The addition of these constellations provides large gains for the surveying community in several different ways.
First, the additional satellites mean more signals to help with the mathematical equations necessary for positional determination. While traditional surveying in the general public’s eye is associated with measurements on the ground, our expansion of services into the air and water relies heavily on GNSS determined positions.
No matter what type of remote sensing equipment is being used (lidar, photogrammetric, sonar, etc.), positional determination for most of those sensors are derived from GNSS-based receivers. Add to these measuring methods the ability to perform operations via remote-controlled or autonomous vehicles in both air and water, and the availability of additional satellite signals enhances the reliability of GNSS-derived data and attributes.
Second, by having more satellite signals to utilize, GNSS receiver manufacturers can improve the software for processing the positional information with greater certainty of accuracy.
Before the introduction of additional constellations and receivers with expanded signal reception, GNSS users relied on less sophisticated software to identify potential “bad” signals that would lead to incorrect positions. While the software generally provided reasonable reliability, it was not foolproof and occasionally would allow bad data to be accepted.
Like most everything tech-related, however, the GNSS industry has benefited from increased computing power to go along with the additional satellite constellations. The latest GNSS receivers can accept well over 500+ signals from a variety of sources (including land-based transmitters). The software used to reduce all that data has increased in complexity along with number of those data sets.
Complex computations that were once limited to mini-computers or even mainframes are now being completed on handheld data collectors in minuscule timeframes compared to their predecessors.
The software has also been enhanced to analyze the data in real-time, compute the likely position of the receiver and notify the user of potential incorrect or “spoofed” data from any number of satellites.
Considering that many of the remote-sensing sources now collect millions of points based upon one GNSS-based position, the need for increased positional verification has become a critical issue. By having many more constellations to provide signals for positional data, the percentage of establishing a correct location for each data point has increase significantly.
The improved computing power and verification ability of today’s GNSS software is helping to eliminate errors in positional accuracy and instill more confidence in the surveyor’s data collection activities.
Add to these additional constellations the planned installation of more land-based signal providers to augment or provide a backup plan for satellite systems, and it’s clear that the future is quite bright for GNSS-based receivers and data collection for everyone — especially the surveying community.
The history of wireless communication
While surveyors marvel at the advancements of GNSS-based measurement, it pales in comparison to the rapid growth of modern technology with cellular devices. Notice I didn’t write cellular phones, as the technology has quickly established itself as much more than voice communication. Before we lay out the future of cellular data networks, let’s take a step back and see how this type of communication has revolutionized GNSS-derived data collection for surveyors and others.
Two-way, CB and shortwave ham radio
1947 two-way radio advertisement. (Image: Motorola)
The technology behind wireless communication goes back several decades, but didn’t become a mainstream system until the late 1970s and early 1980s. Motorola is known as the early force behind the two-way radio system, but the base and remote transmitters were not cost effective for small businesses. This type of system was also limited to single-purpose radios with individual crystals wired within that only allowed specific frequencies to be transmitted.
Another type of communication used by some was the citizens band radio, affectionately referred to as CB radio. This radio was limited to 40 channels and didn’t allow for private transmission between two parties. During the 1970s, the use of the CB radio was not limited to long-haul truck drivers — many people used the medium for basic communication.
Vintage CB radio ad from Radio Shack. (radioshackcatalogs.com)
Telephone service during these times was still costly and long-distance calls were not cost-effective, so many found the CB radio as an alternative to conventional phone service. Looking back now, it is not a stretch to classify this type of broadcasting as a primitive social media precursor to today’s methods but limited to live chats and no visuals.
Another method of transmission was short-wave radio. This system was like two-way radios with an established base transmitter, but broadcast on public frequencies over greater distances than CB radios. One of the big drawbacks was the upfront costs, which were much more significant than the other radios. Even more expensive was outfitting a vehicle with a shortwave system, so cost was the biggest limiting factor for this mode of communication.
Pagers of all shapes and sizes
Motorola’s Pageboy pager. (Photo: Motorola)
The popularity of telephone-based pagers didn’t hit its zenith until the early 1990s, but the technology and actual use dates to the early 1960s. The first commercial pager was produced by Motorola in 1964 and called the Pageboy. There was no screen or display; the user was notified by a variety of tones preset for distinct situations or needs. As this technology advanced, variations in screens, message types and even two-way communication became possible.
By 1994, there were more than 60 million pagers in use, but a change was in the technological wind; cellular phones were marching toward the mainstream.
While the concept of wireless telephone communication existed in several laboratories around the world for years, the first big breakthrough was made by researcher Martin Cooper, who developed a prototype cellular device for Motorola in the early 1970s. He famously made the first public cellular phone call on April 3, 1973, to Joel Engel, head of research at Bell Labs, during a walk in New York City. Cooper and Engel were engaged in a rivalry to develop the first commercially available cellular phone with the Motorola DynaTAC prototype being the first to make a successful call.
However, the rush to get cellular phones to market took longer than anticipated. It wasn’t until the introduction of the Motorola DynaTAC 8000 in 1983 (available to the public in March 1984) that the reality of wireless phones came to life. The cost of wireless freedom came at a price: $3,500 for a brick-sized phone that took 10 hours to charge for 30 minutes of use. The cost of the service was also expensive due to the limited cellular infrastructure.
The next decade brought us expanded cellular coverage along with miniaturization of phone; each subsequent model provided more features and longer battery life. From the Nokia “candy bar” to the Motorola Razr, the cellphone era had engulfed the mainstream, but more changes were ahead for mobile communications.
The late 1990s saw the introduction of the cellphone as a computer modem, with limited email connectivity and primitive internet browsers built into the operating systems. But like many electronic technologies that came before, the cellphone would begin a radically different life in the mid-2000s.
Enter the smartphone to help us dummies
The IBM Simon Personal Communicator and charging base. (Photo: IBM/public domain)
Officially, the smartphone has been in existence since 1992 with the creation of the Simon Personal Communicator from IBM. At a cost of $1,100, with a monochrome screen that was 4 ½ x 1 ½ inches, the Simon allowed the user access to email and faxes (remember those?) along with the phone function — but users had to make it fast; the battery only lasted an hour. IBM sold 50,000 of these units before pulling the plug on the project, but it started the trend toward mobile telephones with a graphical interface and extended uses beyond the standard verbal communications.
Just like the Apple Newton was the failed precursor to the Palm Pilot, various tablets and eventually today’s smartphone platform, the Simon broke ground and established new directions for future communication.
The early 2000s introduced us to the Blackberry personal digital assistant (PDA) and phones from Research in Motion (RIM), a small electronic communications company from Ontario, Canada. RIM started small with a two-way paging system that became popular in Europe and quickly morphed into cellular devices that worked on the DynaTAC network used by Motorola.
A recent model Blackberry PDA. (Photo: Blackberry)
By the mid 2000s, their devices became affectionately known as the “Crackberry” as users became addicted to the functions and capability of this communication tool. These devices were popular with business users as the security encryption was considered more effective than any of the other communication apparatuses of the day.
By 2009, Blackberry had reached the zenith of the mobile device market (second only to the conventional mobile-phone platform dominated by Nokia) but began a rapid decline due to proliferation of the next big thing — the touchscreen smartphone.
After Apple’s introduction of the iPhone in 2007, followed by a crowd of Android-powered phones in 2008 and beyond, Blackberry’s market share has been reduced to a small niche group.
And now, why this relates to the surveyor…
The rollout of Steve Job’s dream of combining Apple’s industry-defining iPod with mobile phone capability revolutionized not only the way we communicate — it has redefined our everyday environment. Many of the tasks we accomplish every day have been incorporated into a smartphone application, which brings us back to the reason this article is directed at surveyors: the device that hangs on your belt or rests in your pocket is revolutionizing the way today’s surveyors work.
Not that long ago, the only navigational devices available were large, expensive and difficult to use. Now, nearly everyone owns a device with GNSS capability. When we combine the ever-expanding number of devices along with the increased coverage of GNSS satellite constellations, the ability to georeference any piece of data to greater precision and accuracy is improving.
Surveyors need to embrace this technology within their smartphones to increase their efficiencies. At the same time, we need to help educate the public on why having better smartphone location capability doesn’t mean the masses can perform their own boundary analyses. For more information on this subject, see the GPS World July 2017 article.
Surveyors should embrace the smartphone as an important tool; the introduction of new survey-grade GNSS receivers that use an app for the user interface will soon become commonplace.
Several GNSS manufacturers have introduced receivers that exclusively use a smartphone and app for data collection, eliminating the need for a dedicated (and usually proprietary) data collector for obtaining centimeter-level location data. I’m not advocating that the surveying community throw their existing systems in the trash in favor of these newer receivers, but the data-collection techniques utilized by smartphones can increase efficiency and reduce equipment costs.
The Mi 8 smartphone offers dual-frequency capability. (Image: Xiaomi)
Another reason to pay attention to the smartphone as a location tool will be the expanded use of dual-frequency chipsets to provide even higher accuracies. One of the fastest growing phone makers worldwide is Xiaomi, based in Beijing, China, which introduced the Mi 8 phone with a dual-frequency GNSS chip. This chip frequency reception (E1/L1+E5/L5) is targeted to embrace the Galileo and GPS constellations for increased accuracies (within a decimeter), well beyond the current norm for smartphones (typically 1-3 meters, plus or minus). For the surveyor, having this capability in their pocket can greatly increase efficiencies, especially when used during reconnaissance efforts. I believe many more phone manufacturers will begin to incorporate dual-frequency chips in their future models to increase location accuracies for users and take advantage of upcoming network enhancements.
Speaking of network enhancements, let’s talk 5G as a gamechanger.
The latest buzz in the general population’s lexicon is 5G and how it will push high-speed internet to all corners of the world. While this is a possibility, it means much more to the surveyor than meets the eye. Yes, there will be increased cellular coverage in places that previously lacked it, or only had limited access, but 5G means much more than that.
Image: NTT DOCOMO Inc. 5G white paper.
Let’s refresh our view of what cellphone coverage currently means to the surveyor. The use of cellular-based RTK receivers has been greatly expanded due to the increased coverage of 4G LTE signals throughout the world, but it’s still scarce is some parts. This is mostly due to the transmission of cellular signals being required from towers and higher placed antennas with powerful transmitters. These transmitters are costly and typically owned and installed by the larger telecom companies, so placement is traditionally in more populated areas.
Enter 5G — while it will provide enhancements for all users, it will be revolutionary for the surveyor. 4G cell coverage was a broad and powerful signal from large transmitters; 5G cellular service consists of smaller cell signals placed in a tight grid of broadcast positions. These transmitters will be more cost effective for many telecom providers and will increase data reception for many users. For surveyors, the additional coverage of 5G will make possible the use of cellular-based RTK GNSS data collection in places not previously possible.
Besides the extended coverage of 5G, the 10-fold speed of the new data transmission protocol compared old 4G LTE creates many possibilities for information collection growth. Soon it will be possible for a field personnel and the office staff to be linked in real time during the data collection process.
From boundary-point recovery to complex topographic surveys, a field crew’s work can be supervised and reviewed while being completed, allowing for instantaneous analysis and guidance from senior staff. This process will allow for more oversight, quality control and mentoring of field staff than is possible for today’s remote crew operations. The new technology will also allow for reduced timeframes when crews are required to provide field data for tight deadline requests and gives us a method of instant feedback on the amount and quality of the data collection.
Some may see this improvement in connectivity as an avenue for office staff to be intrusive on their field activities, but I see this as an opportunity for improved quality control and increased team interaction. More connected teams can lead to improved efficiencies and overall increases in productivity, profitability and morale among team members.
From outside to inside
Another breakthrough created by 5G will be the enhancement of indoor georeferenced location services. By having several transmitters placed throughout a facility, trilateration will be possible to provide more accurate location information for places not typically available to surveyors.
Depending on the accuracy needed and placement of the cell providers, it will possible for surveyors to use devices designed for remote sensing (laser scanners, lidar, SLAM, etc.) and collect georeferenced data with greater accuracy in relation to a known coordinate system. This by-product will also aid rescue and medical providers during emergencies to help pinpoint individuals through their cellphone connection more accurately than before.
5G is more than just bringing YouTube videos to your phone faster; it will improve the data collection process of all shapes and sizes. Surveyors will not get left out, but we will need to be ready to take advantage when it comes online. For more on the 5G revolution, see the GPS World February 2018 article on this topic.
As surveyors, just when we think that technology can’t take us further, we blink and change happens. Moore’s law stated (depending on which revision) that technology would double the number of transistors every one to two years. While some may say that technology is making Moore’s law obsolete, I believe the creativity being used to invent new processes based upon the technology is holding strong.
I, for one, look forward to many more enhancements to follow in the coming years. Surveyors be ready; the future is here.
Skycatch has announced an on-premise data processing and GNSS base station, the Skycatch Edge1, manufactured in partnership with DJI and now available worldwide.
Edge1 base station. (Photo: Skycatch)
Tested and optimized for the Skycatch Explore1 and DJI Phantom 4 RTK drones, the self-positioning Edge1 allows commercial drone users the ability to process and receive data without the need for internet or cellular connectivity, the company said.
Field teams can fly their drone, process the data and receive centimeter-level data outputs in 30 minutes or less, directly to a tablet. 2D maps and 3D point clouds are available for viewing and sharing directly from the tablet.
The Edge1 concept began as a companion to the Skycatch Explore 1 drone. Now, a new generation of the Edge1 will support all DJI drones, including the recently released DJI Phantom 4 RTK, and will process any 2D geotagged images.
In addition to a survey-grade GNSS base station, the Edge1 includes built-in WiFi, LTE, reliable sub-5-centimeter accuracy, and delivers high-quality data outputs, the company added. Built around a state-of-the-art compute module, the Edge1 is also capable of running deep learning algorithms to extract more insights from collected data in near real time.
“It’s truly a revolutionary product that we’re excited to make available to the DJI community, and the construction and mining industry at large,” said Christian Sanz, founder & CEO of Skycatch. “With the partnership and support of DJI, the Edge1 will be assembled with precision execution in their world-class manufacturing facility, and will be available faster to the customer.”
“As the commercial drone industry has grown, the amount of data collected by our enterprise users is unprecedented,” said Jan Gasparic, director of strategic partnerships at DJI. “We are glad to work with Skycatch to manufacture the Skycatch Edge1 GNSS base receiver, enabling enterprise customers, especially those in the construction industry, to process data from their DJI drones on-site and in real-time.”
Skycatch is an industrial data collection and analytics company focused on indexing and extracting critical information from the physical world, using a combination of hardware, software and artificial intelligence. Built for enterprise, its turnkey solutions are deployed across global project sites with largest construction, mining and energy companies.
Harxon is showcasing high-precision positioning GNSS antennas and its latest wireless data transmission technologies for surveying applications at Intergeo, Oct. 16-18, in Frankfurt, Germany.
Image: Harxon
X-Survey is an 4-in-1 OEM antenna for both navigation and communication in the real-time kinematic (RTK) surveying applications. It provides standard Wi-Fi, Bluetooth, 4G, and multiple-constellation signal reception for GNSS positioning.
Its 3D design ensures a higher phase center stability and longer communication distance at a 360-degree direction, while lowering the impact of electromagnetic interference (EMI), hence increasing the overall machine efficiency and simplifying the RTK integration, the company said.
Photo: Harxon
The smart eRadio is a long-range and highly efficient radio modem designed to support RTK applications in surveying and precision agriculture. It can automatically identify RTK serial baud rate and provide a plug-and-play form for easy connection between eRadio and RTK.
According to Harxon, the eRadio’s diagnostic reporting software can configure data and update radio status, allowing users to effectively deal with potential issues. In addition, it is equipped with the unique ETALK communication protocol that increases the communication distance by 20 percent.
Other Harxon GNSS products showcased at Intergeo are for UAVs and precision agriculture, as well as surveying.
The D-Helix antenna HX-CHX600A is featured with its patented D-QHA technology.
Both 3D structured and mini-designed choke-ring antennas HX-CGX601A and HX-CGX611A can be used for base-station communication.
The multi-constellation survey antenna GPS 1000, frequency hopping modem HX-DU2017D and external radio modem HX-DU8608D are also popular products for high-precision performance.
Duro Inertial is a ruggedized version of Swift Navigation’s Piksi Multi dual-frequency real-time kinematic (RTK) GNSS receiver combined with CRL’s SmoothPose sensor fusion algorithm, which fuses GNSS and inertial measurements into a combined solution.
The blending of GNSS and inertial measurements provides a dead-reckoning capability that allows Duro Inertial to provide a highly accurate, continuous position solution during brief GNSS outages and to deliver a robust precision navigation solution in harsh GNSS environments.
Duro Inertial is an evolution of Swift and CRL’s first joint product, Duro. Building on the on-board MEMS inertial measurement unit (IMU) that exists in Duro today, Duro Inertial harnesses CRL’s loosely coupled (LC) sensor fusion algorithm, SmoothPose, to blend GNSS and inertial inputs, providing a smoother, more available and more robust position, velocity and time (PVT) solution, the companies said.
Duro Inertial seamlessly blends CRL’s SmoothPose GNSS+INS algorithms with Swift Navigation’s Starling Positioning Engine to deliver a highly-accurate LC positioning solution even in GNSS / RTK denied environments.
The inertial aiding feature can operate with RTK, autonomous GNSS and satellite-based augmentation system (SBAS) position solutions from Starling. Duro Inertial also inherits the full set of features from Duro and Piksi Multi including the light-weight SBP communication protocol, interoperability with legacy protocols such as NMEA output and RTCMv3 input, compatibility with RTK corrections services such as Skylark, Swift’s Cloud Correction Service and many third-party corrections services, and quad-constellation dual-frequency RTK navigation.
The combination of Duro Inertial’s positioning accuracy and its ruggedized enclosure that protects against weather, moisture, vibration, dust and water immersion makes it suitable for construction, mining, logistics, positive train control, robotics and agriculture applications.
“We are excited to introduce our second collaboration with Carnegie Robotics and build on the success of the Duro ruggedized receiver launched last year,” said Timothy Harris, co-founder and CEO of Swift Navigation. “The combination of Carnegie Robotics’ advanced inertial technology and robotics expertise with Swift’s positioning solution will enable an even broader customer segment to benefit from highly-accurate positioning.”
“Duro Inertial is the culmination of our partnership with Swift over the past two years,” added John Bares, CEO of Carnegie Robotics. “Working together we are able to deliver a consistent and highly-accurate positioning solution to benefit a variety of robotics and industrial applications.”
Duro Inertial is scheduled to be available at for purchase in the fourth quarter and is now available for select customer testing.
DT Research has launched the DT372AP-TR rugged real-time kinematic (RTK) tablet, a lightweight military-grade tablet that offers RTK to enhance the precision of position data derived from satellite-based positioning systems.
The tablet enables 3D point cloud creation with centimeter-level accuracy to meet the high standards required for scientific-grade evidence in court, the company said.
The DT372AP-TR RTK tablet is military-grade with an IP65 rating, yet lightweight — offering the versatility to be used in the field, office and vehicles.
A dual-frequency GNSS module is built into the tablet, which uses real-time reference points within 1- to 2-centimeter accuracy to position 3D point clouds created from aerial photogrammetry, using GPS, GLONASS and Galileo satellites. Users can measure with the RTK GNSS positioning directly using an external antenna for better survey-grade precision.
“We designed a more compact tablet that still offers all the functionality of a rugged RTK tablet, to give ultra-mobility to law enforcement and first responders who are already weighed down with heavy equipment,” said Daw Tsai Sc.D., president of DT Research. “With programmable side buttons and directional pad, this tablet combines ease of use with a small form factor and centimeter level accuracy, there is nothing in the market now in the same category that can offer this combination.”
The DT372AP-TR RTK tablet is compatible with existing survey and GIS software for mapping applications and brings together an advanced workflow for data capture, accurate positioning and data transmitting.
The tablet can be used in a variety of scenarios, including:
Forensic mapping. Public safety teams, investigators and crash reconstructionists can use the DT372AP-TR to accurately collect measurements that are scientifically and legally defensible by using the real-time centimeter reference points to position 3D point clouds created from aerial photogrammetry or take stand-alone measurements. By using the tablet with a drone for crime and crash scene investigation, cost goes down while accuracy and speed improve, helping to clear areas faster, thereby improving overall public safety.
Land surveying. Surveyors can use the DT372AP-TR RTK tablet to measure the altitudes, angles and distances on the land surface so that they can be accurately plotted on a map to determine property boundaries, construction layout and mapmaking.
E-construction. Construction workers can manage the collection, review, approval and distribution of highway construction contract documents in a paperless environment using the DT372AP-TR RTK tablet.
The tablet has been purpose-built for precision mapping in a variety of environments and includes the following features and capabilities:
Dual-frequency GNSS module. GNSS L1 and L2 RTK that receives GPS, GLONASS, Galileo, BeiDou and QZSS signals up to 372 channels
High-performance CPU and Windows OS. Intel Pentium processor with Microsoft Windows 10 IoT Enterprise with 8 GB of RAM.
Brilliant sunlight-readable display. A 7-inch LED-backlight, high-brightness (800 nits) sunlight-readable screen with capacitive touch and 1280 x 800 resolution.
Superior wireless connectivity. Long-range Class 1 Bluetooth option powers connectivity up to 1,000 feet.
Mobile broadband option. For best field connectivity, there is an option for 4G mobile broadband for LTE, HESPA+, GMS/GPRS/EDGE, EV-DO, Rev A and 1xRTT.
Military standards. For use in harsh environments, the tablet is fully ruggedized to meet the highest durability standards with an IP65 rating, MIL-STD-810G for vibration and shock resistance and MIL-STD-461F for EMI and EMC tolerance.
High-capacity hot-swappable battery pack. Delivers 60 or 90 watts for up to 15 hours of continuous mobile communications.
Camera option. The optional back camera offers 5 megapixels, CMOS sensor and auto focus to capture project progress or record crash and crime scene details.
Accessories. External antennas, pole mount cradles, battery charging kits and digital pens.
Galileo and BeiDou observation data are now included with Trimble VRS Now subscriptions in North America.
Photo: Trimble
The addition of the Galileo and BeiDou constellations allow users to make use of more satellites, enabling more robust performance when working in harsh GNSS environments such as in urban canyons and under canopy, the company said.
Trimble VRS Now in North America fully supports GPS, GLONASS, QZSS and now, Galileo and BeiDou satellite systems.
The service is powered by the Trimble Pivot Platform GNSS real-time network software, Trimble said. As a true five-constellation solution, it delivers improved real-time positioning performance for customers in North America.
VRS Now is designed for surveying, mapping and GIS, construction and agriculture professionals who require high-accuracy positioning in their workflows.
Adding Galileo and BeiDou observation data provides significant benefits by enabling users to:
Operate in environments where traditional GPS + GLONASS systems’ performances are limited
Improve accuracy and reliability of GNSS solutions
Minimize the effects of multipath and interference
“By including Galileo and BeiDou data, customers can achieve greater accuracy and positioning performance than ever before,” said Patricia Boothe, vice president of Trimble’s Advanced Positioning Division.
With the addition of North America, Trimble VRS Now networks worldwide now support all five GNSS constellations. Besides North America, coverage is available throughout Europe, Australia and New Zealand when using a compatible GNSS receiver or display.
Subscriptions are available through Trimble’s Authorized Business Partners or Trimble’s online store at tpsstore.trimble.com.
VRS Now provides positioning professionals with instant access to real-time kinematic (RTK) and post-processing (PP) corrections using a network of permanent (fixed) continuously operating reference stations (CORS). Professional management and monitoring 24/7 by a global operations team provides peak performance and high reliability, Trimble said.
Geneq Inc. has introduced the Net20 Pro, a robust system designed for Continuously Operating Reference Station networks.
The Net20 Pro’s efficiency and flexibility will provide high-quality data for users interested in the proximity and reliability of a reference station while eliminating real-time kinematic (RTK) corrections service charges, the company said.
The Net20 Pro uses multi-frequency, 555-channel technologies in a rugged casing to deliver accurate and effective positioning data even in a harsh environment.
The receiver can be configured for correction data reception in client mode to calculate a fixed RTK position and to monitor the antenna position while continuing to work as a GNSS reference server.
With its NTRIP Caster software, the Net20 Pro provides superior connectivity with an unlimited number of mount points, Geneq said. Users can have permanent transmission of RTK corrections with a simple local internet connection from a LAN working network.
Equipped with an internal memory of 32 GB with an additional 32 GB external memory, the Net20 Pro provides enough storage space for permanent recording even for a 100-Hz high data sampling rate.
The Net20 Pro comes with an ergonomic and easy-to-manage web user interface that features software upgrade, status and settings management, as well as data downloading via smartphone, tablet or other internet-enabled electronic devices.
SXblue, also known as Geneq, has introduced its SXblue ToolBox, an Android application for SXblue GNSS receivers.
Using the SXblue ToolBox, receiver users can view and analyze the position data provided by the SXblue receiver and metadata related to its location. The user can send commands that enable or disable some features, including systems in use, mask angle or differential angle, and constellation in use, including GPS, GLONASS, Galileo, BeiDou and SBAS.
The SXblue ToolBox is also an NTRIP client capable of connecting to a NTRIP server for real-time kinematic (RTK) corrections and thus allow the receiver to issue very accurate location information. The application is able to record, save and transfer the raw data from the GNSS receiver, allowing post-processing activities on computers for surveying and geomatics professionals.
The application has been developed with special consideration for modern mobile device development and attention to user and dealer feedback, the company said.
The application includes a series of audible and visual alarms configurable by the user to determine the thresholds of the information provided by the SXblue GNSS receiver.
Main features of the SXblue ToolBox include:
Display of location information and quality of the position data
Skyplot of all-in-view constellations: GPS, GLONASS, Galileo, BeiDou, QZSS, SBAS
Log raw data
NTRIP/DIP client for receiving RTK corrections
Terminal to send commands and view the output data of the SXblue device
Audible and visual alarms
Activation of options and licenses via the application.
Lane errors in a three-lane road, giving lane determination (yellow triangle). (Photo: Pavel Vinnik/Shutterstock.com)
A lane-keeping system uses a sensor-fusion engine integrating GPS and an IMU with a two-stage map-matching algorithm. The system does not require explicit lane-level geo-referencing, saving massive storage required for lane-level spatial reference information, and reduces the computational complexity of the map-matching algorithm.
By Mohamed M. Atia, Carleton University and Allaa Hilal, Intelligent Mechatronics Systems
Lane determination is an important feature of advanced automotive navigation and guidance systems. It can be used in advanced driving assistance systems (ADAS), lane-departure warnings, and self-driving cars to perform lane-level, turn-by-turn guidance and control. It is also valuable information for telematics applications such as usage-based insurance. Lane-estimation systems have been dominated by vision and infrared sensors. Light detection and ranging (lidar) has also been used as a lane-determination technique. Those systems depend on visually recognizable features and landmarks that may not be available in some areas due to weather conditions or unstructured environments.
In addition, visual data processing may need specialized accelerators and parallel computing platforms to satisfy real-time constraints. To explore other alternatives, several research projects have started to investigate the feasibility of using low-cost global positioning and navigation technologies such as GPS, micro-electromechanical systems (MEMS) inertial measurement units (IMU) and geographical information systems (GIS) as an alternate lane-determination technology. However, most current systems have two main drawbacks: they use high-end RTK GPS, which suffers from coverage issues, and they use explicit lane geo-referencing, which leads to increased storage and processing.
Here we investigate the feasibility of using standard GPS fused with low-cost MEMS-IMU and a road network that includes lane information but not explicitly storing geo-referenced lane-level links.
The accuracy of Standard Positioning Service (SPS) GPS is within 3.351 meters (m) with a 95 percent confidence level. Figure 1 shows the results of standard single-point positioning test for a stationary receiver.
FIGURE 1. Standard GPS 2D position accuracy in a stationary test. (Figure: Mohamed M. Atia and Allaa Hilal)
The standard lane width in North America is approximately 3.6 m, requiring an unbiased precise positioning solution of much less than 1.8 m. If a safety margin of 50% is considered, unbiased precise positioning of less than 0.9 m is needed. Therefore, a standard SPS GPS technology may not be precise enough to accurately determine the vehicle’s lane. Advanced precise positioning technology like differential GPS (DGPS) can be used with high-resolution lane-level maps to achieve the lane determination.
However, these techniques may require additional cost/infrastructures and extra processing. To target a lower cost lane-determination system, this work suggests the fusion of measurements from a standard GPS, MEMS IMU and road-level network.
The work includes a sensor-fusion engine that is developed to integrate GPS and IMU using a loosely coupled extended Kalman filter (EKF). Then, a two-stage map-matching algorithm using a Hidden-Markov-Model (HMM) and a least-squares (LS) regression is developed.
The system does not require explicit lane-level geo-referencing; consequently, it saves massive storage required to save explicit lane-level spatial reference information, and it reduces the computational complexity of the HMM algorithm by reducing the number of road segments the HMM needs to decode. The overall system is illustrated in Figure 2.
FIGURE 2. Illustration of the proposed system. (Figure: Mohamed M. Atia and Allaa Hilal)
PROBLEM DEFINITION
A geometric illustration of the problem is shown in Figure 3. The road-network map is represented as a set of connected segments. Each road segment is defined by a straight line segment with a start position and end position. Curved roads are approximated by a sufficiently large number of straight line segments. Based on this notation and geometric illustration, the estimation problem that this article is addressing is the determination of the lane on which the vehicle is moving.
FIGURE 3. Illustration of the lane determination problem. (Figure: Mohamed M. Atia and Allaa Hilal)
Map-Matching with Hidden-Markov Model. The simplest map-matching method, point-to-curve-matching, is performed by searching for the nearest road segments within a threshold from the current vehicle’s position. The distance is calculated between the vehicle’s position and its projection on the map segment. However, this approach is sensitive to state estimation errors, and it fails at intersections, joins, branches or dense parallel roads. For example, Figure 4 shows a situation where biased GNSS position measurements exist, and the wrong map segment is selected because of the pure dependence on the distance metric only (for instance, D1 is less than D2).
FIGURE 4. Wrong map-segment selection in intersection. (Figure: Mohamed M. Atia and Allaa Hilal)
To avoid these errors and to improve map-matching accuracy, the matching criteria must include several constraints such as map topology (connectivity), vehicle dynamics, road geometry and legal direction of motions. In this work, to consider these constraints, we keep a recent portion of the vehicle motion history and use it in the matching criteria. This strategy is known as curve-to-curve matching.
To process a noisy stream of data, the HMM algorithm is used. A Markov model is a stochastic model that describes a sequence of states. The transition from one state to another can be modeled by a conditional transition probability.
If the states are not directly observable (hidden) but can be indirectly observed through a sequence of outputs, the process is called a Hidden Markov Process. The HMM in this case is characterized by the transition probability and an emission probability that represents the probability that a given state generates a certain observable.
Both transition probability and emission probability constitute the Bayesian network of HMM. A fundamental problem of HMM is that, given a sequence of outputs, what is the best sequence of states that explains the observed outputs? This problem is solved by selecting the sequence of states that maximize the HMM probability.
This estimation process, called decoding, is solved using the Viterbi algorithm. In the proposed system, the hidden states represent map links, and the observable outputs are the vehicle poses. To develop a robust map-matching framework, the vehicle pose history, roads geometry, and map topology constraints must be considered. Therefore, the emission and transition probabilities of an HMM are formulated such that they reflect all of these constraints. The Bayesian network of the HMM for our system is shown in Figure 5. The vehicle states (poses) is obtained from the INS/GNSS filter described shortly.
FIGURE 5. Hidden Markov model for vehicle’s state map-matching. (Figure: Mohamed M. Atia and Allaa Hilal)
In the proposed work, the length of the processed buffer of the vehicle’s state is determined based on the traveled distance. The aim is to accumulate a reasonable geometric knowledge about the trajectory segment that enables the HMM to accumulate enough geometric and topological constraints to be able to select the correct sequence of road segments in difficult intersections, joins and exit/entry roads.
EKF GNSS/INS SYSTEM
The navigation problem can be modeled as a dynamic system of states vector x(t) as follows:
(1) (2)
(Figures: Mohamed M. Atia and Allaa Hilal)
where f(.) is a nonlinear dynamic model, w(t) is a stochastic system noise vector, u(t) is a control signal vector that triggers the transition from current state to a future state, y(t) is external measurements vector (observables), h(.) is a nonlinear measurement model and v(t) is a stochastic measurement noise vector. Using first-order Taylor series approximation, (1) and (2) can be linearized as follows:
(3) (4)
(5)
(6)
(Figures: Mohamed M. Atia and Allaa Hilal)
A Kalman filter calculates an optimal estimation of provided that w(t) and v(t) are zero-mean Gaussian noise vectors with covariance matrices defined by:
(7)
(8)
and δx is the error vector with zero-mean and a covariance matrix P defined by:
(9)
Using zero-hold discretization where derivative is approximated by:
(10)
where T is the sampling time, equations involving HMM probability can be written in discrete form as follows:
(11)
(12)
The optimal estimation of the error vector, δxk, given measurements, yk, is calculated using two steps: prediction,
(13)
(14)
and update,
(15)
(16)
(17)
(Figures: Mohamed M. Atia and Allaa Hilal)
In INS/GNSS systems, the dynamic system state transition (x(t)) is triggered by IMU sensors (accelerometer and gyroscopes) while GNSS measurements are used as observables (y(t)). The observables update in our case is GNSS position and velocity. Therefore, the measurement error model is defined as follows:
(18)
where H is defined as follows:
(19)
Lane Estimation. When the road segments have been accurately selected based on the filtered vehicle’s pose, the projection of the vehicle’s positions on segment lanes can be easily calculated knowing the lane widths and number of lanes. The sum of squared errors for each lane is then calculated by:
(20)
where N is number of epochs, and pv is the projection of vehicle’s position on lane. The lane associated with the minimum error is selected as the designated lane.
(Figures: Mohamed M. Atia and Allaa Hilal)
Lane-Change Detection. If a lane change occurred within the processed buffer of data, the least-squares regression will not converge to the correct lane. Therefore, the buffer needs to be partitioned at the lane-switch locations. Thus, a lane-change detection module is developed. In this work, a lane-change detection method is designed based on capturing the patterns of the vehicle’s orientation and raw gyroscope measurements. The heading and raw gyroscope measurements during lane changes are shown in Figure 6 and Figure 7.
FIGURE 6. Vehicle’s heading during lane change to left. (Figure: Mohamed M. Atia and Allaa Hilal)FIGURE 7. Vehicle’s gyroscope measurements during lane change to left. (Figure: Mohamed M. Atia and Allaa Hilal)
The general pattern that the lane-change module detects is a peak or a valley in azimuth accompanied by a peak/valley or valley/peak sequence in the gyroscope measurements. To detect peaks and valleys, the standard deviation of a moving window of data is calculated and compared to a peak/valley threshold. If both gyro and azimuth peak/valley sequence are consistent and matched with the pattern described above, a lane change is declared.
Two algorithm phases of processing are then applied:
Acquisition Phase. GNSS and IMU measurements are fused in the main EKF, and HMM map-matching is performed and a lane is estimated. The innovation sequence of the main EKF, which is the difference between the predicted state and GNSS updates, is calculated over a buffer of data. If the innovation sequence is within a small threshold and no lane change has been detected, the acquisition phase is concluded and the tracking phase begins.
Tracking Phase. Two EKF filters are initiated. One EKF accepts position updates from the projection of the vehicle’s position on the selected lane, and the other EKF accepts GNSS position updates only. A discrepancy measure is evaluated between the two EKF instances for a short window of time. If this discrepancy measure is higher than a threshold, a temporary GNSS deviation is assumed and the system keeps reporting the current lane as the designated lane. If GNSS measurements started to be centered again on the new lane, a lane change is confirmed and the output of the first EKF instance will be the correct state. Otherwise, this lane change is declared as false and the second EKF output is the correct output. The overall block diagram of the proposed system is shown in Figure 8.
FIGURE 8. Overall block diagram of the proposed system. (Figure: Mohamed M. Atia and Allaa Hilal)
TESTS AND RESULTS
The proposed system has been tested on a computer connected to a GNSS receiver and an automotive MEMS-grade IMU, and road-network map data. A GPS-enabled camera was installed to capture video of the experiment, to be used as a ground truth to verify the results of our algorithms. Sensor specifications are given in Table 1 and Table 2. The effect of level arm (distance between IMU and GNSS antenna) was not considered in this implementation.
TABLE 1. GNSS receiver accuracy. (Table: Mohamed M. Atia and Allaa Hilal)TABLE 2. IMU specifications. (Table: Mohamed M. Atia and Allaa Hilal)
Three testing trajectories were collected during July 2015 through Highway 400 from Wilson Avenue in the south to Davis Drive in the north. Approximately 65 kilometers of trip data was collected. The data included some urban areas but was mostly open sky. It also included challenging road intersections and road joining/branching points. The experimental setup was designed such that the system automatically started when the vehicle’s engine was turned on. A Linux OS was installed on the gigabyte computer box, and a data acquisition firmware was configured to automatically begin when the computer starts. Measurements from the GNSS receiver at 1 Hz and the IMU at 50 Hz were synchronized on the computer. The main algorithm including GNSS/INS fusion and map-matching was developed in native ANSI C language for efficient processing. Original raw IMU data was set to 50 Hz down-sampled to 5 Hz. Within this interval, the real-time system could fetch map information from a cached database file, perform basic prediction steps and implement the forward calculation of a Viterbi algorithm (including calculation of emission and transition probabilities) that is needed for the HMM map-matching step.
Lane-Determination Results. The lane estimation results were logged and time-tagged. Using the video recording, the ground truth lane-level solution was visually inspected and manually recorded in a file. Since both the video camera and the proposed INS/GNSS/maps systems log data tagged by GPS time, synchronization between ground truth and the estimated lane were possible. The estimated lanes were visually inspected record by record and results were saved in an Excel sheet. The results were written into a time-tagged file where each row can be easily visually inspected by looking at the portion of images corresponding to the same time-tag. The time-tag used was the UTC-time contained in the NMEA GNSS raw measurements. The overall accuracy of the proposed system in lane determination is shown in Table 3.
TABLE 3. Lane-estimation accuracy. (Table: Mohamed M. Atia and Allaa Hilal)
Figure 9 and Figure 10 show example snapshots of the visual inspection software tool developed to evaluate the accuracy of the system. As can be seen in the figures, an image of the road that indicates the correct lane is displayed in the upper graph, while the estimated lane information is displayed along with road information including lane errors in the lower graph. Figure 10 shows that the system can identify the correct lane when the number of lanes is increased.
FIGURE 9. Lane errors in a three-lane road. (Figure: Mohamed M. Atia and Allaa Hilal)FIGURE 10. Lane errors in a four-lane road. (Figure: Mohamed M. Atia and Allaa Hilal)
CONCLUSION
This work described a low-cost lane-level positioning system using a conventional GNSS receiver, MEMS IMUand commercially available road-level network without the need for explicit spatial storage of lanes. The research used a conventional GNSS receiver and MEMS IMU with a computationally efficient two-stage HMM-based map-matching algorithm that avoids the explicit use of lanes as hidden states, which significantly reduces the size of the HMM network and consequently enhance its real-time performance. The proposed system provides an alternative lane determination method without the need for computationally expensive vision/lidar methods that may fail in dark, foggy or dynamically changing environments. The work showed extensive experiments under different road sections, showing an average lane-determination accuracy of 97.14%.
ACKNOWLEDGMENTS
This work was first presented at ION International Technical Meeting, January 2018.
MANUFACTURERS
The system comprises an Intel Celeron N2807 1.58-GHz Mini PC connected to a u-blox EVK-7P kit GNSS receiver and an automotive MEMS-grade IMU 3D space sensor IMU from YOST Labs, and road-network map data from HERE. A GPS-enabled HP f310 car camcorder captured video.
MOHAMED M. ATIA received a Ph.D. in electrical and computer engineering from Queen’s University at Kingston. He is assistant professor and founder/director of the Embedded Multi-sensor Systems research laboratory in Carleton University, Ontario, Canada.
ALLAA HILAL received a Ph.D. degree in electrical and computer Engineering from the University of Waterloo. She is director of the innovation and emerging technology department at Intelligent Mechatronic Systems, a connected-car company based in Waterloo, Canada.
