Topcon Positioning Group introduces Topcon ContextCapture, powered by Bentley Systems, a reality modeling software solution that will be offered with Topcon UAS (unmanned aerial systems).
The system is designed for mapping, construction and surveying professionals to quickly turn simple photographs and or point-cloud data into true-to-life, highly detailed 3D models for use throughout a project lifecycle.
“The offering will include Topcon ContextCapture Standard and Topcon ContextCapture Advanced,” said Charles Rihner, vice president of the Topcon GeoPositioning Solutions Group. “The standard package will be bundled with Falcon 8 and Sirius Basic/Pro and allows operators to process data from these UAS into textured 3D reality meshes, point clouds and orthophotos. ContextCapture Advanced allows users to process data from any UAS. It also includes ContextCapture Editor, which enables operators to take advantage of all project data by integrating reality meshes and point clouds, into infrastructure workflows. The result is access to a wide variety of reality modeling tools to help increase productivity.”
Context Capture software by Topcon.
The ContextCapture Advanced integration includes computer-aided design (CAD), inspection, GIS, civil engineering, and survey workflows on desktop and mobile devices, in multiple formats.
“This represents the next step in the Topcon and Bentley collaboration to advance the concept of constructioneering — allowing users to start from a reality-captured survey context and leverage and update their digital engineering models throughout the construction process, and finally deliver the as-built infrastructure in real time,” Rihner said.
“We are excited to bring to market this new joint offering that enables greater efficiency and productivity in the global construction market,” said Phil Christensen, Bentley vice president of reality modeling. “Our reality modeling solution for mapping, construction, and surveying professionals will enable them to quickly turn UAS imagery into engineering-ready 3D reality models that can be used immediately and updated throughout the construction lifecycle. Since we announced our constructioneering partnership last November, we see this as only one of many new integrations between Bentley and Topcon that will enable better project outcomes.”
Sensor role reversal: Lidar with its superior performance can replace GNSS in the integration solution by providing fixes for the drifting inertial measurement unit (IMU). Tests show its potential for terrain-referenced navigation due to its high accuracy, resolution, update rate and anti-jamming abilities. A novel algorithm uses scanning lidar ranging data and a reference database to calculate the navigation solution of the platform and then further fuse with the inertial navigation system (INS) output data.
Recent rapid advances in laser-based remote sensing technologies, including pulsed linear, array and flash lidar systems, have fostered the development of integrated navigation algorithms for lidar and inertial sensors. In particular, trajectory recovery based on lidar point-cloud matching can provide valuable input to the navigation filter. Lidar/INS integrated navigation systems may provide continuous and fairly accurate navigation solutions in GNSS-challenged environments, on a variety of platforms, such as unmanned ground vehicles, mobile robot navigation and autonomous driving.
In the case of airborne lidar/INS applications, the free inertial navigation solution is used to create the point clouds, which are subsequently matched to a digital terrain elevation model (DEM). The results are fed back to the platform navigation filter, providing corrections to the free navigation solution. This solution may be used to recreate the point cloud to obtain better surface data.
However, depending on the lidar data acquisition parameters, INS drift during the time between the two epochs when point clouds are acquired could be significant. Besides the shift in platform position, the drift in attitude angles could more severely impact point-cloud generation, producing a less accurate point cloud and subsequently poor matching performance.
This article describes a new lidar positioning approach, where the scale-invariant feature transform (SIFT)-based lidar positioning algorithm is used to match between the lidar measured point cloud and the reference DEM. The matching process is aided with fuzzy control: SIFT-based lidar positioning algorithm with Fuzzy logic (SLPF), where the threshold for SIFT is adaptively controlled by the fuzzy logic system.
Based on the geometric distribution and the range difference variance of the matched point clouds, fuzzy logic is applied to calculate the threshold for the SIFT algorithm to extract feature points; thus the optimal matched point cloud is extracted in several iterations. When there are enough matched points in the final output of the SLPF, the platform position is calculated by using the least squares method (LSM). Next, for trajectory estimation, when applying the SLPF algorithm, frequent lidar updates can be used to correct small cumulative errors from the INS sensor measurements. A Kalman filter fuses the results of the SLPF algorithm with the INS system.
This integrated algorithm can handle situations when there are less than three matched feature points being extracted by the SLPF algorithm, and yet they could still contribute to obtain a better navigation solution. Simulation results show that, compared to the existing algorithms, the proposed lidar/INS integrated navigation algorithm not only improves the position, speed and attitude-determination accuracy, it also makes the lidar less dependent on INS, which makes the navigation system work longer without exceeding a particular drift threshold.
LIDAR ALGORITHM
To eliminate the influence of INS error on the lidar positioning system, instead of creating a measured DEM based on INS ortho-rectification, we directly map the range data measured by lidar to the local stored DEM data. If a successfully matched feature point can be obtained, it means that we can get a point with absolute position and relative range towards the platform, which is similar to the satellite in GNSS positioning. After scanning of one area by lidar, when three or more such matched feature points, if not on a line, can be obtained, then we are able to form a full rank equation with the unknown variables of the platform position x, y and z.
However, due to the effect of affine transformation, the standardized range dataset collected by lidar is significantly different from the elevation dataset belonging to the same area. Figure 1 shows an example of the large difference between the two datasets from the same area when the pitch angle of the platform is equal to 5° and the flying height is 2,000 m. In this situation, the traditional flooding algorithm or constellation feature point matching algorithm is incapable of extracting matched feature points from such different datasets.
Figure 1. Comparison between SR and DEM data from the same area.
In response, we introduce the SIFT algorithm to the elevation map-matching procedure. Designed for image matching, the SIFT algorithm is invariant to scale, rotation and translation, and it is robust to affine transformation and three-dimensional projection transformation to a certain extent. Although SIFT is often used in image matching, each pixel from the image is a numerical point, which, in fact, has no difference with elevation data point. Before applying the SIFT, some processing on the lidar measured range data must be done.
LIDAR RANGE DATA
The scanning information of the lidar measured points are (α, β, r), where α is the angle between the laser beam and the negative Z-axis of the platform body frame, β is the angle from the laser beam to the plane of axis and Z-axis in body frame, r is the range between the laser head and the measured target, as shown in the opening figure.
Due to the terrain relief, the lidar range data are irregularly spaced. Therefore, it is necessary to interpolate the collected data. Here we apply the Natural Neighbor Interpolation method.
SIFT Algorithm, Fuzzy Control. For the lidar positioning algorithm, which is based on the absolute position and relative range of the ground-matched feature points, a point cloud with sufficient number of points of good geometric distribution is needed. In practice, however, the terrain undulation and the attitude of the airplane will affect the quality of the point cloud and the accuracy in the matching process. In addition, the selected threshold in the SIFT algorithm plays an important role on the quality of the matched point cloud.
A Monte Carlo simulation, shown in FIGURE 2, illustrates the impact of the threshold on the number of successful matched points (normalized) and mismatched rate. For obtaining better matched point clouds, we have introduced a SIFT terrain matching algorithm assisted by fuzzy control, as shown in FIGURE 3.
Figure 2. Relationship effect of threshold on the number of successful matched point (normalized) and error matched rate.Figure 3. Working principal diagram of SIFT terrain matching algorithm based on fuzzy control.
The algorithm mainly consists of two fuzzy logic controllers. Controller 1 calculates the initial threshold for the SIFT algorithm according to the gridded SR data terrain undulation degree λ, and the angle Θ between Z-axis in body-frame and Z-axis in navigation frame.
Controller 2, which is responsible to adaptively changing the threshold at each epoch, has two inputs. The first one is the Normalized Points Area (NPA), which represent the geometric condition of the matched point cloud. The other one is the Relative Range Difference Variance, which indicates if a mismatch has happened. When the final matched feature point cloud is obtained, and the number of points is greater than or equal to 3, then the LSM is used to calculate the position of the platform.
INS/LIDAR NAVIGATION
Loosely and tightly coupled integration are the most common methods in navigation systems. Given the characteristics of the proposed positioning algorithm, the classical integrated navigation algorithm needs to be modified. In the loosely coupled approach, the lidar is unable to aid INS when flying through a flat region and/or flying with a large tilt angle, because the proposed lidar positioning method may have difficulty in extracting enough matched points to calculate a position.
In the tightly coupled method, as the output frequency of matched point cloud is low and the geometry of the matched feature points is relatively poor, the integrated system may be extremely unstable. Here we propose a combined loosely and tightly (CLT) integrated navigation algorithm that when the lidar positioning algorithm can extract enough matched points for a navigation solution, the lidar-calculated navigation solution is used as the main observation.
However, when the matched points are not sufficient to obtain a navigation solution, the baseline vector of the matched point that is closer to the projection of the platform center to the surface will be utilized as the observation. In this solution, lidar can still provide a certain degree of aid to the INS, once extracting matched feature points, even if less than 3.
SIMULATION ANALYSIS
In the simulation experiment, the 3D DEM data of 0.5-meter resolution is obtained from an open source named EOWEB. Then the DEM data is resampled to a higher resolution of 0.1 meter, which is used to generate the simulated, irregularly spaced, measured range data. On the basis of the original DEM (0.5 meter resolution), the proposed lidar positioning algorithm and lidar/INS integrated navigation algorithm are verified and compared with the traditional methods.
Simulation of Lidar Algorithm. As shown above, the successfully matched points rate is very important for positioning, as once a mismatched point occurs, it may lead to a faulty navigation solution. In the simulation, the proposed SLPF is simulated under the condition of different aircraft tilt angle ϴ, from 0° to 10° with a step of 1° , at 5,000 different positions, which is the same simulation condition as in Figure 2. Comparison is made with the traditional constellation feature matching based lidar positioning algorithm (CLP) and the SIFT based lidar positioning algorithm without fuzzy control (SLP). The successfully matched points rate and the NPA value are shown in Figure 4.
Figure 4. Successful points matched rate and the NPA value results under different aircraft attitude condition from three different algorithms.
As can be seen from the figure, along with the increasing platform attitude angle, the successfully matched points rate of all the three algorithms has declined. However, compared to the CLP, both SIFT-based algorithms have a higher success matching rate due to the more stringent feature-point extraction approach. And due to the adjustable threshold mechanism, the SLPF could remove some of the mismatched points by raising the threshold; thus it is superior to the common SIFT algorithm in performance. The NPA values of the extracted point cloud from the three algorithms are shown in Figure 4(b). With the increased attitude angle, the NPA value of the matching feature point cloud decreases in all three algorithms. The CLP algorithm, however, is more sensitive to the projected range data, which makes the number of successful matching points drop sharply, and further affect geometric distribution of the point cloud. The gap between the SLPF and SLP shows that the fuzzy control module can help improve the geometric structure of the feature point cloud.
Figure 5 shows the positioning error when applying the three different matching algorithms at 5,000 different areas. The SLPF algorithm is better than the other two algorithms in all directions. When the platform’s attitude angle reaches about 10 degrees, the north and east positioning accuracy of SLPF algorithm is still about 8 meters, and the height positioning accuracy is about 0.2 meters. The reason that the height positioning error is far less than the north and east positioning error is because of the matching point cloud distribution. Due to the airborne lidar scanning mechanism, the matched point cloud is all located in a relative small area at the bottom of the platform, resulting in the great component value in the height direction of each matched feature point baseline vector in the G matrix, and then affect the final positioning accuracy.
Figure 5. Positioning accuracy under different aircraft attitude conditions with different algorithms.
Table 1 shows some detailed information as average number of matched points (ANMP) and matched points position error (MPPE) using the three methods. The MPPE is calculated in 3D space. It can be seen that when the tilt attitude is small, comparing to the CLP method, although the number of matched points extracted by SLPF is less, the matched points position accuracy is still much better, leading to a better localization result. Moreover, with the increasing platform tilt attitude, CLP and SLP have more difficulty in maintaining the number and accuracy of the matched points.
Lidar/INS Algorithm. To validate the feasibility of the proposed integrated navigation algorithm, firstly, the motion trajectory of the platform must be simulated. As shown in Figure 6, the red line is the simulated platform true trajectory, which lasts for 1,400 seconds. During the trajectory, the platform undertakes the different motion states as acceleration, deceleration, climbing, turning and descent. Then the INS output data based on the true trajectory with the frequency of 100 Hz is generated. To verify the calibration performance on the INS in the integrated navigation algorithm, accelerometer and gyroscope drift noise is added to the INS output data. The green line shown in Figure 6 is the INS output data trajectory solution. At the end of simulation, the error to the east direction reaches 500 meters, and the north direction error reaches to more than 2,200 meters.
Figure 6. Comparison between True trajectory and INS calculated trajectory.
At the same time of the INS outputting navigation solution, lidar also scans and calculates the position of the platform with 1-Hz frequency. Note that the speed of the aircraft is from 70 m/s to 100 m/s, and the maximum lidar scanning angle αmax is 20°. Figure 7 and Figure 8 show the number of matched points and the positioning error for each scanned terrain using SLFP. When the platform maintains smooth flying, the number of matched points can reach an average of 10, and the positioning accuracy is relatively high, less than 3 meters. Note, during the period, only in a few epochs are the number of matched points less than five. However, when the platform is climbing or changing flight direction, the number of matched points is obviously decreased due to the large tilt angle of the platform, and so does the number of successful positioning times. In this case, the position error is also increased dramatically, reaching about 10 meters error in east and north, and 0.2 meters error in height. Especially in the course of changing the direction of the flight, shown in Figure 7, during the periods of 720s–800s and 920s–1,000s, due to the larger roll angle, the SLPF could hardly be able to calculate the position through the LSM. During this period the lidar would occasionally output 1 or 2 matched feature points.
Figure 7. The number of the matched points of each lidar positioning epoch.
Figure 8. The positioning accuracy of each lidar positioning epoch.
During the simulation, the CLT and LC methods are used for data fusion and trajectory estimation comparisons. TC method is not added to the comparison because of slow convergence. The data fusion results are shown in Figure 9. It illustrates that the LC method and the CLT method have close positioning accuracy in the case of sufficient matched feature points. As can be seen in conjunction with Figure 8, when lacking matched points, the CLT method is superior to LC on positioning accuracy, especially in the height direction. In addition, the CLT integrated algorithm shows some improvement on the accuracy of estimating speed and attitude.
Figure 9a. Data fusion results using two different integrated algorithms: position determination error.
Figure 9b. Data fusion results using two different integrated algorithms:velocity determination error.
Figure 9c. Data fusion results using two different integrated algorithms: attitude determination error.
Figure 10 shows the position error distribution when using four different lidar/INS integrated navigation methods for data fusion under the condition of different simulation trajectories. In the simulation, 50 1,400-second-long different trajectories, with flat areas, are generated with different platform attitude, velocity or acceleration. As can be seen from the figure, compared to other integrated navigation methods, the CLT method greatly improves the accuracy of navigation.
Figure 10. Position error distribution when using four different lidar/INS integrated navigation method.
During 84.26% of the simulation period, CLT could maintain the position error less than 3 meters; the rate with error that is larger than 15 meters is 1.2%. For the TC method, due to the frequent divergence of the data fusion filter, most of the position estimates are not available. In addition, after flying above a flat area, the voting-based constellation integrated method has poor matched point accuracy and successfully matched rate due to large INS drift error, which makes lidar unable to calibrate the INS. When using the constellation-based method, during only 32.35% of the simulation period, the error is maintained in 3 meters and most of the period, 54.9%, the position error is between 3 to 15 meters.
CONCLUSION
We propose a new lidar matching algorithm based on SIFT, which does not rely on the INS output data to generate measured DEM data, and can adaptively change the threshold of the SIFT algorithm to generate optimal matching between the point cloud and the DEM. Through verification of simulation, the algorithm is compared with traditional lidar/INS integrated navigation methods based on comparing achieved accuracies in estimating position, speed and attitude. Simulation results show that the SLPF algorithm has better reliability for feature points matching and robustness against the platform attitude than the traditional algorithms. The CLT method improves trajectory estimation accuracy, especially when flying over moderately undulating terrain or flying with large roll or pitch angles.
ACKNOWLEDGMENT
This article is based on a paper presented at the ION International Technical Meeting, January 2017. This research used an open-source GNSS/INS simulator based on Matlab, developed by Gongmin Yan of Northwestern Polytechnical University, China.
Haowei Xu is a Ph.D. student at Northwestern Polytechnical University, where he received an M.Sc in Information and Communication Engineering. He is a visiting scholar at The Ohio State University.
Baowang Lian is a professor at Northwestern Polytechnical University where he is also director of the Texas Instruments DSPs Laboratory.
Charles K. Toth is a senior research scientist at the Ohio State University Center for Mapping. He received a Ph.D. in electrical engineering and geo-information sciences from the Technical University of Budapest, Hungary.
Dorota A. Brzezinska is a professor in geodetic science, and director of the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at The Ohio State University.
Microdrones collaborated last summer with the DLRG Horneburg/Altes Land e.V. (German Lifeguard Association) to simulate a mission to rescue a drowning swimmer, demonstrating the life-saving potential of UAVs.
Crowds watched from the banks of the Elbe River as a UAV flew to the person in distress and dropped a compact rescue device called RESTUBE, which automatically inflated. The swimmer was able to grab onto the RESTUBE and float until he could be reached by a lifeguard and brought to safety.
The UAV used in the rescue was the microdrones md4-1000. The quadcopter drone features specially developed motors, carbon fiber housing, efficient batteries, and an integrated GPS system that allow the UAV to fly and stay in position in strong winds over the water.
For the simulation, the md4-1000 was equipped with an imaging camera that streamed live to the specially trained lifeguard operating the drone, allowing him to easily see the precise location to drop the RESTUBE flotation device.
“An adult drowns in approximately 60 seconds and a child in only 30,” said Christopher Fuhrhop, founder and CEO of RESTUBE. “By combining UAVs and RESTUBE flotation devices, we arE able to buy the drowning person valuable time that could very well mean the difference between life and death.”
Other safety possibilities for quadcopters include locating people using thermal imaging cameras and collecting data on the condition of leaking and burst banks on hard-to-reach embankments.
What is the biggest challenge facing the UAV industry? Go to gpsworld.com/17marpoll to give us your opinion by March 22 and you’ll also be entered in a drawing to receive a $50 gift card.
Here are the possibilities on offer, plus an “other” category for you to specify something bigger if you think we’ve omitted anything.
Better quality images and video
Better, smaller, more lightweight sensors (inertial, Lidar, infrared, spectral, etc.)
Integration of other sensors with GPS/GNSS
Applications and command-control on mobile devices: smartphones and tablets
Virtual and augmented reality
Competition from satellite and aircraft imagery/mapping/other
Air traffic control and the FAA regulatory environment
Other (please specify)
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Watch this space for continuing coverage of developments in UAV navigation and related issues, with in-depth reporting from the upcoming AUVSI Xponential conference in May.
The unmanned ground vehicle market (UGV) is estimated to be valued at $1.49 billion in 2016 and is projected to reach $2.63 billion by 2021 with a CAGR of 12.14 percent during the forecast period, according to a new market report.
The report, published by MarketsandMarkets, examines the unmanned ground vehicle market (UGV). The base year considered for the study is 2015, and the forecast period is 2016 to 2021.
The title of the report is “Unmanned Ground Vehicle Market by Application (Defense-ISR, EOD, Crew Integration, Commercial-Agriculture, Field, Domestic, Transportation), Mobility (Wheeled, Tracked), Size, Component, Modes of Operation, Payload & Region — Global Forecast to 2021.” The 193-report includes an in-depth table of contents, 86 market data tables and 64 figures.
The increasing demand for UGVs in the commercial and defense sectors and technological innovations that have created a demand for UGVs to perform complex operations with minimal human intervention and better safety are the major factors driving the UGV market, according to the company’s analysts.
Based on application, the UGV market has been segmented into commercial and defense. The commercial segment of the UGV market is projected to grow at the highest CAGR till 2021. This growth is driven by the increasing demand for domestic and industrial UGVs.
Based on size, the unmanned ground vehicle market has been segmented into micro UGVs, small UGVs, medium UGVs, and large UGVs. The small UGV segment of the unmanned ground vehicle market is projected to grow at the highest CAGR during the forecast period. The demand for small UGVs from both the commercial and defense sectors for their capabilities has enhanced the growth of this segment.
Wheeled UGV and tracked UGV have been considered under the mobility segment of the unmanned ground vehicle market wherein the tracked UGV segment is projected to grow at the highest growth rate. Tracked UGVs are more versatile than wheeled UGVs as they can be operated on difficult terrains and can carry higher amounts of loads, thus leading to its higher demand.
Autonomous mode
In 2012, a second unmanned MTVR was built to evaluate multiple UGVs supervised by a single operator.
The unmanned ground vehicle market is segmented into tethered, tele operated, semi- autonomous and autonomous, based on mode of operation. The autonomous segment is estimated to have the largest share with the highest CAGR in this segment during the forecast period due to their capability of operating without any human intervention.
The software component segment is estimated to grow at the highest CAGR during the forecast period compared to the hardware segment as the customers are looking for sophisticated UGVs, which require advanced software systems.
The UGV market Asia-Pacific is projected to grow at the highest growth rate during the forecast period. The rapid growth of the Asia-Pacific market can be attributed to the increasing investments to develop UGVs for defense as well as commercial applications. The investments are mainly driven by the developments in China, India, Japan and South Korea, which are among the fastest-emerging economies in the world.
Major players
The major players in this market have been identified to be QinetiQ Group Plc. (U.K.), iRobot (U.S.), Northrop Grumman (U.S.), Oshkosh Corporation (U.S.) and Lockheed Martin (U.S.), among others.
The report segments and analyzes the unmanned ground vehicle market on the basis of mode of operations (tethered, tele-operated, semi-autonomous and autonomous), mobility (wheeled and tracked), size (micro, small, medium and large), payload (sensors, lasers, camera, radars and others), application (defense and commercial), and component (hardware and software) and maps these segments and sub-segments across the major regions of the world, namely, North America, Europe, Asia-Pacific, the Middle East and the rest of the world (comprising Latin America and Africa). Brief information on the research methodology for the report can be found in the report description provided on website.
Related Reports
“Military Robots Market by Platform (Airborne, Naval, Land-based), Application (Warfield, Pick ‘n’ Place, Firefighting, Voice-controlled Robotic Vehicle, Metal Detector Robotic Vehicle, Others), & Region — Global Forecast to 2020”
“Unmanned Aerial Vehicle (UAV) Market by Application, Class (Mini, Micro, Nano, Tactical, MALE, HALE, UCAV), SubSystem (GCS, Data Link, Software), Energy Source, Material Type, Payload and Region — Global Forecast to 2022”
The Federal Aviation Administration (FAA) today released an updated list of pilot, air traffic controller, law enforcement and citizen reports of potential encounters with unmanned aircraft systems (UAS). The latest data cover February through September 2016.
Reports of possible drone sightings to FAA air traffic facilities continued to increase during FY 2016. There were 1,274 such reports from February through September last year, compared with 874 for the same period in 2015.
Although the data contain several reports of pilots claiming drone strikes on their aircraft, to date the FAA has not verified any collision between a civil aircraft and a civil drone. Every investigation has found the reported collisions were either birds, impact with other items such as wires and posts, or structural failure not related to colliding with an unmanned aircraft.
Safely integrating unmanned aircraft into the national airspace system is one of the FAA’s top priorities, and the agency wants to send a clear message that operating drones around airplanes and helicopters is dangerous and illegal. Unauthorized operators may be subject to stiff fines and criminal charges, including possible jail time.
The FAA wants operators to know where it’s legal to fly their drones. For current information on where unmanned aircraft can be flown safely, the FAA offers the B4UFLY app, available for iOS and Android smartphones. The app is free and can be downloaded from iTunes and Google Play.
A new system using RF detection of drone radio transmissions to warn of incoming drones is just one of several interesting developments in the unmanned systems sector this month.
While UAS, or drones, continue to proliferate around the world, the majority appear to be used in meaningful and useful applications — earning money, helping disaster relief and in public service applications such as firefighting and police monitoring/tracking the bad guys. And there are those who fly them from the beach, just to get good overheads of the expensive neighborhood — lots of harmless, non-intrusive backyard, conscientious home-grown operations.
But every now and again some bright spark tries to get the best possible picture of a passenger jet on approach or during regular air-traffic maneuvers. Air France just cried foul on Thursday, Feb. 9, when a Boeing 777 on approach into Washington-Dulles Airport caught sight of UAV estimated to be only 100 feet above the aircraft.
Airport Close Call
Now, why would a huge 240-foot-long, 250-ton B-777 even be bothered by a skinny 10- to 15-pound baby drone? Because on approach, an aircraft is dumping lift, reducing altitude, balancing speed — maneuvering a huge beast like a 777 can be quite a delicate operation. Its huge turbofan engines are also spinning really fast even at flight idle, and they still suck in an awful lot of air, so sucking in a stray quadrotor isn’t difficult. They do test these engines for bird ingestion during qualification, but I don’t think anyone has yet put anything like a DJI drone through an engine to see if the engine survives — frozen chickens don’t have any of the hard bits that drones have — and the whirling supersonic blades inside the compressor sections will not take well to foreign objects made of plastic, fiberglass, silicon and metal. Power loss low on approach can easily lead to disaster.
Not to mention that at 700 feet, it’s probably bad news for the ~300 passengers if an engine quits or the guy driving has to unexpectedly jink the aircraft sideways to avoid a darn drone. This low-energy phase of flight involves a delicate balancing act of many parameters, and we don’t need pilots to be distracted from their focus of bringing their aircraft down a narrow landing corridor safely to the runway. Never mind the damage that even a small UAV can do to a multi-million-dollar aircraft. The Federal Aviation Administration (FAA) has mandated that drones fly below 400 feet and stay several miles away from airports for a reason.
Detection and Disabling Drones
Which brings us once again to equipment intended for the detection and disabling of drones. Keeping these pesky, unwelcome intruders away from penetrating airport protection boundaries — or other sensitive areas — is starting to become a business for which significant growth is being forecast, even paralleling the growth of drone sales.
Several significant European agencies have already put Sensofusion radar equipment to work defending their facilities, or are undertaking joint R&D efforts with the company. Installations such as prisons, government, military and community security sites have benefited from a hybrid detection and location solution system known as Airfence.
And, to the point, Sensofusion from Finland was also recently included in a group of companies selected by the U.S. FAA for a cooperative program aimed at the development of drone protection, location and prevention for airports. The other companies added to the FAA Pathfinder Program at the same time were Gryphon Sensors and Liteye Systems. The FAA’s objective is to find a system to deploy to “spot, block and drop the unwanted unmanned aircraft systems” before they get anywhere near the boundary fence, never mind into controlled airport airspace.
The Airfence system starts by using RF detection of drone radio transmissions from over six miles away and immediately raises the alarm in case of an intrusion — even notifying controllers on their smartphones. The system then triangulates the location of the incoming drone and uses what appears to be directional high-power RF transmissions to disrupt the drone’s control link.
For an example of how attention is turning to anti-drone systems, Dedrone in San Francisco, which develops software products designed to detect drones and protect high-value airspace from drone threats, recently secured a whopping $15 million during a round seeking investment funding.
Army’s Shadow Disappears
It could be that a roaming drone might not be wandering at the hands of someone intent on mischief. Operators of a $1.5-million U.S. Army Shadow fixed-wing UAV lost contact during a training flight recently, and it was presumed to have crashed in Southern Arizona within the area of operations.
Shadow launch.
The Army went looking for the bits, but extensive searches found no trace of the elusive Shadow. Turns out that the UAV was eventually found by a hiker stuck up a tree several hundred miles away in Colorado, in the foothills west of Denver. The Army sent local troops and police to recover the errant drone.
So, it seems that it’s not just malicious operators who may cause problems in commercial airspace. When things go wrong, we may also need a means to bring down an off-flight-plan drone. The side-trip for the Shadow apparently may have been brought on by unusually warm, gusty winds blowing into Colorado from the desert southwest on the day the aircraft went missing. Just as well that the tree caught the drone, as Shadows have a flight endurance of eight to nine hours.
General Atomics Seeks Non-Military Opportunities
And now General Atomics (GA) — one of the best-known UAV manufacturers of them all and their turboprop powered Predator — both are looking for opportunities in the “less-military, semi-commercial” world. The UAV that most people picture when someone says drone is probably the Predator, or its successor known as the Reaper.
SkyGuardian UAV.
GA recently announced that its new SkyGuardian UAV is intended to be certifiable to airworthiness requirements. Given that no civilian standards yet exist for this class of large UAV, GA is using published military NATO, UK and German standards and recommendations for its early certification activities. SkyGuardian has benefited from a five-year-long company-funded effort to develop a certifiable UAV. Given that the existing military Predator fleet has altogether flown for almost four million hours, GA should already be ahead of the curve when it comes to proving airframe and systems reliability. The first production aircraft is planned for 2018.
While its clear that GA is using largely military qualification standards and the target market seems to be in support of ground forces, its also aimed at non-military applications, such as border patrol, and quasi-military operations such as police, related security agencies and disaster relief. A maritime patrol version is also planned for coastal and open-water coast-guard applications. SkyGuardian has a lengthy 35-hour endurance, can fly at up to 240 mph and reach altitudes of around 46,000 feet.
Flying Packages?
And Amazon keeps pumping out patents, which give us some indication of what they might be planning for their much-publicized drone delivery system. Its latest patent has Amazon delivery drones arriving at their delivery point, but instead of landing to drop off a package, the package is dropped from the drone in flight.
To ensure that the order doesn’t land in the neighbor’s pool, the package’s descent is controlled by small parachutes, a landing flap or compressed air release. This implies that the package has radio communications with the drone, so the flying packaging isn’t inexpensive. Aerobraking, maneuvering packages — what’s next?
Patent drawing of flying package and parachutes.
These flying packages or their carrier drone are not intended to interfere with commercial aircraft on take-off or approach because Amazon has also supported a drone delivery highway below 400 feet with its own air-traffic control system. But I can’t help thinking that flying packages might be a bit of a stretch. But who knows? The drone industry is demonstrating nothing but innovation!
The GPSdome anti-jammer was developed for civilian applications. It aims to curb situations in which civilian vehicles are stuck “off the grid.” It combats electromagnetic warfare by using null steering, a method of spatial signal processing through which a transmitter can nullify communication jamming. In particular, the product was developed to address the requirements of autonomous cars, drones and unmanned aerial vehicles, all of which depend heavily on GPS to function. Several carmakers have expressed interest in integrating the anti-jammer in their autonomous cars, including Daimler-Mercedes, Ford, Toyota, Hondand BMW and others.
The Aaronia GPS Logger is a six-parameter datalogger designed for recording the position and orientation of RF antennas (such as the Aaronia HyperLOG X, HyperLOG EMI and Magnotracker series) during field investigations. It also is useful for a wide range of non-RF applications where position and movement logging is required. It has sensors in a very small form factor, with a fast data-capture rate of up to 35 logs/second. The logger with built-in battery is 4 x 1.7 x 0.9 inches and weighs 3 oz. The logger starts up in about 30 seconds and features a 66-channel GPS sensor with built-in antenna, offering a position accuracy of six feet, maximum velocity measurements of up to 350 mph and altitude up to 60,000 feet, with a signal sensitivity of –165 dBm. The logger can be used to create an RF heat map including frequency, direction and strength of an RF source with a 360-degree view. All sensor data can be captured at up to 35 readings per second on to a microSD card or via USB streaming. The real-time indication of data makes the Aaronia GPS logger useful for instantly assessing position-variable information.
For consumer GPS processing and smartphone indoor positioning
Photo: Focal Point Positioning
S-GPS is a smartphone-based sensor fusion, machine learning and signal processing suite designed to provide satellite positioning capabilities in urban environments and indoors. With its multipath-mitigation process, S-GPS improves the performance of existing radio-based positioning systems. The fully software-defined solution is aimed at system-on-chip silicon architecture and smartphone receiver front ends. A software upgrade for existing receivers, it requires no extra hardware, dongles or infrastructure to operate. The computational load of S-GPS is comparable to that of existing GNSS processing. The higher sensitivity of S-GPS allows signal tracking to be maintained in traditionally difficult environments, such as deep indoors, where standard devices would fail. This reduces the time spent in acquisition mode in urban areas, leading to significant improvements in battery life in like-for-like tests with standard A-GPS technologies.
The u-blox LARA-R3121 is a single-mode LTE Category 1 modem and a GNSS positioning engine. It is designed for Internet of Thigns (IoT) applications including smart utility metering, connected health and patient monitoring, smart buildings, security and video surveillance, smart payment and point-of-sale systems, as well as wearable devices, such as action cameras. It comes in a land grid array (LGA) package for easy manufacturing, and offers easy migration from u‑blox LTE, UMTS, CDMA and GSM/GPRS modules.
NTS units can detect difference between real and spoofed signals
Photo: OnTime Networks
OnTime Networks has added advanced anti-spoofing technology to its Blueberry and Cloudberry CM-1600 network time server (NTS) product lines. OnTime Networks’ proprietary anti-spoofing algorithms and technology provide not only an alert that GPS is been spoofed, but also the protection that the GPS timing signal is moved over to a highly stable free-running clock, as long as the detected GPS spoofing attack is in progress. Power grids are particularly vulnerable to spoofing, and are increasingly implementing GPS technology to more accurately meter allocations of electricity across the grid. Being even 10 microseconds off could cause power generators to shut down or get damaged.
The GNSS tracking engine of the K708 OEM board with 496 channels is capable of tracking all working and future constellations. Compared with the K5 series OEM boards, the K708 uses an application-specific integrated circuit (ASIC) chip that improves data quality and reduces power consumption. It is designed with strong compatibility and built-in functions, including high-accuracy position, velocity and time (PVT) output, long baseline RTK and reserved webserver service. The K708 is designed for CORS, deformation monitoring systems and related high-accuracy GNSS positioning applications. Signals received include GPS L1 C/A, L2C, L2P, L5; BeiDou B1/B2/B3; GLONASS L1C/A, L1P, L2C/A, L2P; Galileo; and QZSS.
Monitor, manage and evaluate monitoring data, optionally trigger alarms
Photo: Topcon Positioning
The Delta Solutions deformation monitoring system uses several software and hardware components — Delta Link, Delta Log, Delta Watch, Delta Sat and the Topcon MS AXII total station — to provide accurate and reliable monitoring measurements and associated reporting for asset protection. Delta Watch delivers accurate and reliable data in a variety of reporting formats to fit a project’s needs. Data from the total station, GNSS receivers, leveling devices and sensors can be processed and analyzed individually or as a network-adjusted solution. Delta Watch’s optional Delta Sat GNSS processing module allows for stand-alone GNSS monitoring or combined GNSS and total-station network adjustments. Delta Link provides hardware support communication for autonomous operation in the field, managing each power source to maximize system availability, while Delta Log provides an intuitive interface to manage observations, target types and measurement scheduling.
GPS data collector for utilities, mining, forestry, agriculture
Photo: Geneq
The SXPad 1000P is an affordable, rugged handheld GPS data collector specifically designed for mobile GIS users in applications such as water, electric and gas utilities, transportation, mining, agriculture and forestry. The high-performance 1000-MHz device is designed to give professionals the power needed to work with maps and large data sets in the field. It has an IP67 waterproof seal and can survive 5-foot (1.5-meter) drops to concrete. Its 3.7-inch color touchscreen (full VGA) is sharp and is sunlight readable. Standard features include a battery life of more than 10 hours on a charge, 8-GB internal storage, and slots for MicroSD cards and SIM cards as well as Windows Mobile 6.5. The SXPad 1000P also offers a 3.5G cellular modem, Wi-Fi, Bluetooth, video capture and a 5-megapixel camera. It is optimized for GPS/GIS field data collection using its 1-to-3-meter accuracy internal GPS receiver or one of Geneq’s high-performance SXBlue GPS receivers for sub-meter and centimeter-level accuracy.
Glean and share insight from big data, internet of things
Esri ArcGIS 10.5 offers next-generation analytics technology by helping organizations glean insight from enterprise data, big data and the Internet of Things (IoT) and share that insight in intuitive ways. It includes improved capabilities for handling large-scale analytics and big data; a drag-and-drop interface that streamlines the creation of spatial analysis through maps, charts and graphs; and collaboration features to connect and analyze information across the enterprise. The new release is powered by Esri ArcGIS Enterprise, a significant evolution of the technology formerly known as ArcGIS for Server. ArcGIS Enterprise has been updated with improved power to process and analyze large, disparate datasets.
Entry-level device for construction, public safety
Photo: Faro
The Faro FocusM 70 is an entry-level laser scanner for construction building information modeling (BIM) and public safety forensics. Features include an IP54 rating for use in high particulate and wet weather, high-dynamic-range imaging, an acquisition speed of almost 500,000 points per second and extended temperature range. Data captured can be used with various third-party software packages. The Faro FocusM 70 is specifically designed for both indoor and outdoor applications that require scanning up to 70 meters and at an accuracy of +/– 3 millimeters.
PingNav ADS-B OUT GNSS navigation unit. Photo: uAvionics
PingNAV is a small, light ADS-B OUT compliant navigation source. ADS-B (Automatic Dependent Surveillance – Broadcast) helps aircraft operators sense and avoid possible collisions. ADS-B is mandated by the FAA for all aircraft in the U.S. National Airspace by 2020. PingNAV supports GPS, GLONASS, Galileo and QZSS, and has a battery backup for quicker position initialization. Dual static ports forpressure altimeter readings and integrated security and integrity technologies include receiver autonomous integrity monitoring (RAIM) and satellite-based augmentation system (SBAS) to detect and correct errors improving accuracy, reliability and availability.
The Ping200S is a small, light, FCC-approved full range mode C and mode SAutomatic Dependent Surveillance-Broadcast (ADS-B) transponder. At 50 grams, power consumption is low enough to be powered by battery pack for 2 hours, yet is powerful enough to provide visibility to other aircraft and UAVs up to 200 miles away, at which point it implements sense and avoid for drone operations in the national airspace. The ping200S is designed to meet the requirements of TSO-C199 as a Class A Traffic Awareness Beacon System.
Defense-proven to disrupt and neutralize hostile UAVS
Photo: Liteye Systems, Tribalco
The AUDS counter-UAS defense systemhas been field proven to detect, track and defeat malicious and errant unmanned aircraft systems (UAS) or drones. The fully integrated system has achieved TRL-9 status following the successful mission deployment of the AUDS system with the U.S. military. TRL-9 is the highest technology readiness level that a technology system can attain. The AUDS system — developed by Blighter Surveillance Systems, Chess Dynamics and Enterprise Control Systems — can detect a drone six miles (10 kilometers) away using electronic scanning radar. It tracks the UAV using precision infrared and daylight cameras and advanced video tracking software before disrupting the flight using a non-kinetic inhibitor to block the radio signals that control it. The detect, track and defeat process typically takes 8–15 seconds. Using AUDS, the operator can effectively take control of a drone and force a safe landing. The AUDS system works in all weather, day or night, and the disruption is flexible, proportional and operator controlled.
For UAV manufacturers to add flight time, extend battery life
Photo: Texas Instruments Sample build.
Two circuit-based subsystem reference designs can help manufacturers add flight time and extend battery life to quadcopters and other non-military consumer and industrial drones used to deliver packages, provide surveillance or communicate and assist at long distances. The 2S1P Battery Management System (BMS) reference design transforms a drone’s battery pack into a smart diagnostic black box recorder that accurately monitors remaining capacity and protects the Li-Ion battery throughout its entire lifetime. Designers can use the drone BMS reference design to add gauging, protection, balancing and charging capabilities to any existing drone design and improve flight time. A second reference design helps manufacturers create drones with longer flight times and smoother performance. It helps electronic speed controllers achieve the highest possible efficiency with performance for speeds more than 12,000 rpm (> 1.2 kHz electrical) including fast-speed reversal capability for more stable roll movement.
The CMA-5024 GPS landing system sensor meets the requirements for an instrument-flight-rules civil-certified GNSS. The European Geostationary Navigation Overlay Service (EGNOS) augments GPS to provide an extremely accurate navigation solution that will support all flight operations from en route through localizer performance with vertical guidance (LPV) CAT-l equivalent approach. The CMA-5024 is compliant with and completely supports EGNOS/SBAS, from departure, en-route navigation and all EGNOS/SBAS LPV precision approaches, and complies with published Communication Navigation Surveillance/Air Traffic Management (CNS/ATM) navigational mandates.
A new variant of Qualcomm’s connected car reference platform uses its gigabit-class Snapdragon X16 LTE modem to help car manufacturers deliver high-speed, high-quality and reliable connectivity for advanced telematics and connected vehicle services. It supports peak download speeds up to 1 Gbps. The reference platform allows carmakers to integrate additional wireless and networking technologies, including Wi-Fi, Bluetooth, Bluetooth Low Energy and GNSS, with optional support for dedicated short-range communication (DSRC) and cellular-V2X. The platform includes a module reference design for the Snapdragon X16 LTE modem to help automotive suppliers accelerate development. The reference platform integrates quad-constellation GNSS and 3D dead-reckoning location solutions, and is designed to manage concurrent operation of multiple wireless technologies using the same spectrum frequencies.
CAD model of the antenna system: The antennas will be arranged so that the center of mass is at the center of the tube. Each antenna will be counterbalanced. (NASA)
Researchers at NASA’s Armstrong Flight Research Center have designed an antenna-mounting platform to provide users satellite-based tracking functions for unmanned aerial vehicles. The platform integrates multiple capabilities onto one low-cost platform.
In August 2016, NASA signed a license agreement with Mobile Antenna Platform Systems Inc. to commercialize the portable antenna platform.
The platform is built to rotate 60 pounds of antennas, transmitters and receivers and eliminate the need for additional load-balancing hardware. A smaller version can be flown on a plane, greatly extending the telemetry link range without requiring more power from the aircraft.
Auto tracking software uses the target’s GPS location to coordinate and maintain a line-of-sight link as great as what the telemetry system can support.
NASA researchers originally developed the technology for use with research UAVs, which often involve multiple transmitters and receivers on the aircraft and on the ground, with multiple antennas that must be pointed at a single UAV.
The platform is a middle ground between the low-end tracking platforms that support only one antenna and expensive, high-end options designed for military use.
Besides research, the platform could be used in marine communications, satellite tracking in multiple frequencies and weather balloon tracking, NASA said.
Powered by 120 VAC, the platform moves all of the antennas simultaneously in continuous rotation in azimuth and vertical ±180°, effectively tracking a line-of-sight object up to 20 miles away or further, limited by transmit power and antenna configuration.
It is designed for use with any moving system needing to transmit large quantities of data over one or more RF links. RF signals can include video, command and control, and signals to and from the UAV as well as the research data of interest.
The platform design includes:
a horizontal bar with antenna mounts
a platform head containing the motors and gears
an antenna stand containing electrical slip rings and cables to connect to the radios, motors and external computer
a microcontroller interface to drive the motors and receive antenna commands from the software
Its user interface runs on Microsoft Windows and enables the tracking antenna to be interfaced to any ground station that can provide the GPS coordinates of the target being tracked in real time and the GPS coordinates of the tracking antenna.
Platform benefits
According to NASA, the antenna platform offers these benefits:
Portability. Lightweight components and a small profile allow the platform to be carried by a single person.
Simplicity. Its unique design eliminates the need for additional load-balancing hardware, simplifying setup.
Versatility. Up to 58 pounds (26 kg) of multiple antennas from various manufacturers in any combination (including Yagi-Uda, dish/parabolic, omnidirectional, patch/microstrip) under 10 W can be accommodated
Low Power Use: Using a smaller motor that is faster than those on other platforms requires less power to achieve continuous rotation.
Low Cost: The overall system is estimated to cost less than $5,000.
Rolls-Royce and VTT’s vision of futuristic land-based control center, known as the Future Operator Experience Concept or oX. (Concept: Rolls-Royce)
Rolls-Royce and VTT Technical Research Centre of Finland Ltd. have signed a strategic partnership to design, test and validate the first generation of remote and autonomous ships.
The partnership, established in November 2016, combines and integrates the two company’s expertise to make such vessels a commercial reality.
Rolls-Royce is pioneering the development of remote controlled and autonomous ships and believes a remote controlled ship will be in commercial use by the end of the decade. The company is applying technology, skills and experience from across its businesses to this development.
VTT is an expert in ship simulation and the development and management of safety-critical and complex systems in demanding environments such as nuclear safety. It combines physical tests, such as model and tank testing, with digital technologies, such as data analytics and computer visualization.
They will also use field research to incorporate human factors into safe ship design. As a result of working with the Finnish telecommunications sector, VTT has extensive experience of working with 5G mobile phone technology and wi-fi mesh networks. VTT has the first 5G test network in Finland.
Working with VTT will allow Rolls-Royce to assess the performance of remote and autonomous designs through the use of both traditional model tank tests and digital simulation, allowing the company to develop functional, safe and reliable prototypes.
Two remote-controlled ship prepare to pass. (Artist’s concept: Rolls-Royce)
“Remotely operated ships are a key development project for Rolls-Royce Marine, and VTT is a reliable and innovative partner for the development of a smart ship concept,” says Karno Tenovuo, vice president of ship intelligence for Rolls-Royce. “This collaboration is a natural continuation of the earlier user experience for complex systems (UXUS) project, where we developed totally new bridge and remote control systems for shipping.”
“Rolls-Royce is a pioneer in remotely controlled and autonomous shipping. Our collaboration strengthens the way we can integrate and leverage VTT’s expertise in simulation and safety validation, including the industrial Internet of Things, to develop new products and in the future, enable us to develop new solutions for new areas of application as well,” says Erja Turunen, executive vice president for VTT.
Ship Intelligence will make greater use of ship systems and sensors to enhance both crew and vessel operating efficiency. (Rolls-Royce)
Datumate has released DatuSurvey version 5.1 for both Professional and Enterprise editions of the software. DatuSurvey (formerly DatuGram 3D) turns drone- and camera-based images to accurate, georeferenced 2D maps and 3D models, which saves the need for expensive and risky field work and expedites deliveries, according to Datumate.
DatuSurvey Professional V5.1 now also includes:
Ground Control Points Hints – Once the model is built with the minimal requirement of 3 GCP’s on two images each, the system will start showing hints for selected GCP on all images it is not marked in. This will make the GCP marking easier and faster.
Differentiating Clusters in Map View – Different clusters are now shown in different colors in the map view.
DatuSurvey Enterprise V5.1 now also includes:
Dense Point Cloud Generation Quality – Dense Point Cloud may now be generated at four different density levels as specified by the user.
Mesh and Texture Support – Dense Point Cloud may now be generated with mesh or with textured mesh. Mesh and texture may be exported to OBJ format.
True Orthophoto Export Quality – Orthophoto may now be generated at four different resolutions.
Visualization Viewer Improvement – 3D Viewer is able to handle up to 100 million points. Thus, viewing an excellent quality model with mesh and texture.
Volume Calculation Improvement – Volume calculation was improved to allow definition of stockpiles right on the dense point cloud, including physical and base surfaces. The definition process is now faster and easier, and the volume calculation of more precise.
Ground Control Points Hints – Once the model is built with the minimal requirement of three GCP’s on two images each, the system will start showing hints for selected GCP on all images it is not marked in. This will make the GCP marking easier and faster.
Differentiating Clusters in Map View – Different clusters are now shown in different colors in the map view.
