GPS World

Tag: UAV

  • GPS-lidar fusion with 3D city models

    GPS-lidar fusion with 3D city models

    A GPS-lidar fusion technique implements a novel method for efficiently modeling lidar-based position error covariance based on features in the point cloud. The fusion uses a three-dimensional (3D) city model to detect and eliminate non-line-of-sight (NLOS) GPS satellites to improve global positioning.

    The technique has potential application in UAV missions such as 3D modeling, filming, surveying, search and rescue, and package delivery.

    By Akshay Shetty and Grace Xingxin Gao, University of Illinois

    Unmanned aerial vehicles (UAVs) commonly rely on GPS for continuous and accurate outdoor position estimates. However, in certain urban scenarios, additional onboard sensors such as light detection and ranging (lidar) are desirable due to errors in GPS measurements. To fuse these measurements it is important, yet challenging, to accurately characterize their error covariance. We propose a GPS-lidar fusion technique with a novel method for efficiently modeling the position error covariance based on surface and edge features in point clouds. We use the lidar point clouds in two ways: to estimate incremental motion by matching consecutive point clouds; and, to estimate global pose (position and orientation) by matching with a 3D city model. For GPS measurements, we use the 3D city model to eliminate NLOS satellites and model the measurement covariance based on the received signal-to-noise-ratio (SNR) values. Finally, all the above measurements and error covariance matrices are input to an unscented Kalman Filter (UKF), which estimates the globally referenced pose of the UAV. To validate our algorithm, we conducted UAV experiments in GPS-challenged urban environments on the University of Illinois at Urbana-Champaign campus.These experiments demonstrate a clear improvement in the UAV’s global pose estimates using the proposed sensor fusion technique.

    SITUATION

    Emerging applications in UAVs such as 3D modeling, filming, surveying, search and rescue, and package delivery all involve flying in urban environments. In these scenarios, autonomously navigating a UAV has certain advantages such as optimizing flight paths and sensing and avoiding collisions. However, to enable such autonomous control, we need a continuous and reliable source for UAV positioning. In most cases, GPS is primarily relied on for outdoor positioning. However, in an urban environment, GPS signals from the satellites are often blocked or reflected by surrounding structures, causing large errors in the position output.

    In cases when GPS is unreliable, additional onboard sensors such as lidar can provide the navigation solution. An onboard lidar provides a real-time point cloud of the surroundings of the UAV. In a dense urban environment, lidar can detect a large number of features from surrounding structures such as buildings.

    Positioning based on lidar point clouds has been demonstrated primarily by applying different simultaneous localization and mapping (SLAM) algorithms. In many cases, algorithms implement variants of iterative closest point (ICP) to register new point clouds.

    APPROACH

    The main contribution of this article is a GPS-lidar fusion technique with a novel method for efficiently modeling the error covariance in position measurements derived from lidar point clouds. Figure 1 shows the different components involved in the sensor fusion.

    Figure 1. Overview of sensor fusion architecture.

    We use the lidar point clouds in two ways: to estimate incremental motion by matching consecutive point clouds; and, to estimate global pose by matching with a 3D city model. We use ICP for matching the point clouds in both cases.

    For the lidar-based position estimates, we proceed to build the error covariance model depending on the surrounding point cloud. First, we extract surface and edge feature points from the point cloud. We then model the position error covariance based on these individual feature points. Finally, we combine all the individual covariance matrices to model the overall position error covariance ellipsoid.

    For the GPS measurement model, we use the pseudorange measurements from a stationary reference receiver and an onboard GPS receiver to obtain a vector of double-difference measurements. Using the double-difference measurements eliminates clock bias and atmospheric error terms, hence reducing the number of unknown variables. We use the global position estimate from the lidar-3D city matching to construct LOS vectors to all the detected satellites. We then use the 3D city model to detect NLOS satellites, and consequently refine the double-difference measurement vector. We create a covariance matrix for the GPS double-difference measurement vector based on SNR of the individual pseudorange measurements.

    We implement a UKF to integrate all lidar and GPS measurements. Additionally, we incorporate orientation, orientation rate and acceleration measurements from an onboard inertial measurement unit (IMU). Finally, we test the filter on an urban dataset to show an improvement in the navigation solution.

    LIDAR-BASED ODOMETRY

    ICP is commonly used for registering three-dimensional point clouds. It takes a reference point cloud q, an input point cloud p, and estimates the rotation matrix R and the translation vector T between the two point clouds. Different variants of the algorithm generally consist of three primary steps.

    Matching. This involves matching each point pi in the input point cloud, to a point qi in the reference point cloud. The most common method is to find the nearest neighbors of each point in the input point cloud. For our application, a kDtree performs best since the two point clouds are relatively close to each other.

    Defining Error Metric. This defines the error metric for the point pairs. We choose the point-to-point metric, which is generally more robust to difficult geometry than other metrics such as point-to-plane. The total error between the two point clouds is defined as follows:

      (1)

    where N is the number of points in the input point cloud p.

    Minimization. The last step of the algorithm is the minimization of the error metric with regard to the rotation matrix R and the translation vector T between the two point clouds.

    We use ICP to estimate the incremental motion of the lidar between consecutive point clouds. Figure 2 shows our implementation of ICP to estimate the lidar odometry.

    Figure 2. The input to ICP is a reference point cloud q and an input point cloud p as shown in (a). The algorithm calculates the rotation matrix R and the translation vector T such that the error metric E is minimized. (b) shows the reference point cloud q and the transformed input point cloud R • p + T.

    MATCHING LIDAR, 3D MODEL

    We generate our 3D city model using data from two sources: Illinois Geospatial Data Clearinghouse and OSM. The Illinois Geospatial Data were collected by a fixed-wing aircraft flying at an altitude of 1700 meters, equipped with a lidar system including a differential GPS unit and an inertial measurement system to provide superior global accuracy. Since the data were collected from a relatively high altitude, it primarily contains adequate details for the ground surface and the building rooftops. In order to complete the 3D city model, we need additional information for the sides of buildings. We use OSM to obtain this information. OSM is a freely available, crowd-sourced map of the world, which allows users to obtain information such as building footprints and heights. Figure 3 shows a section of the 3D city model for Champaign County.

    Figure 3. Section of the point cloud for Champaign County dataset. (Left) shows the 3D city model using only the Illinois Geospatial Data. (Right) fhows the model after incorporating building information from OpenStreetMap.

    To estimate the global pose of the lidar, we match the onboard lidar point cloud with the 3D city model using ICP, in these steps:

    • Use the position output from onboard GPS receiver as an initial guess. If position output is unavailable, use the position estimate from the previous iteration as an initial guess. For orientation, use the estimate from the previous iteration. Thus, we obtain an initial pose guess .
    • Project the onboard lidar point cloud pL to the same space as the 3D city model qcity using .
    • Implement ICP, to obtain the rotation RL and translation TL between the two point clouds. Use this output to obtain an estimate for the global pose .

    Figure 4 shows the results of implementation of the above method. While navigating in urban areas, the GPS receiver position output used for the initial pose guess might contain large errors in certain directions. This might cause ICP to converge to a local minimum, depending on features in the point cloud pL generated by the onboard lidar.

    Figure 4. Global pose estimation with the aid of 3D city model. (Left) shows the intial position guess (red dot, with term in red outlined box) and the onboard lidar point cloud pL projected on the same space as the 3D city model qcity. (b) shows the updated global position (green dot, with term in green outlined box) after the ICP step. We see an improvement in the global position, as the point cloud matches with the 3D city model.

    To evaluate how our lidar-3D city model matching algorithm performs in such challenging cases, we test it in two different urban areas as shown in Figure 5. We begin by selecting a grid of initial position guesses up to 20 meters away from the true position. With an adequate distribution of features, ICP is able to correctly match the two point clouds and provide an accurate position estimate after matching. In contrast, when there’s an urban scenario with a relatively poor distribution of features, ICP is unable to estimate the position accurately.

    Figure 5. Lidar-3D city model matching in two different urban areas. We begin with a grid on initial position guesses (red) around the true position (black). In (a) and (b), there are adequate features. The position estimates after matching (blue) converge to the true position. In (c) and (d) the feature distribution is relatively poor. The position estimates after matching (blue) are parallel to the building surface.

    MODELING ERROR COVARIANCE

    We model the lidar position error covariance as a function of the surrounding features. In urban environments, we typically observe structured objects such as buildings, hence we focus primarily on surface and edge features in the point cloud. We extract these feature points based on the curvature at each point. Points with curvature values above a threshold are marked as edge points, whereas points with curvature values below a threshold are marked as surface points. (For detailed discussion of the algorithms involved, see GPS-LiDAR_AkshayShetty-algorithms.

    For each surface feature point, we first compute the normal by using 9 of the neighboring points to fit a plane. We model the error covariance ellipsoid with the hypothesis that each surface feature point contributes in reducing position error in the direction of the corresponding surface normal. Additionally, we assume that surface points closer to the lidar are more reliable than those further away, because of the density of points.

    For each edge feature point, we first find the direction of the edge using the closest edge points in the scans above and below. We model the error covariance ellipsoid with the hypothesis that each edge feature point helps in reducing position error in the directions perpendicular to the edge vector. A vertical edge, for example, would help in reducing horizontal position error. Additionally, we assume that edge points closer to the lidar are more reliable than those further away, again because of the density of points. Figure 6 shows sample error covariance ellipsoids for a surface point and an edge point.

    Figure 6. Position error covariance ellipsoid for surface and edge feature points. The exact sizes of the ellipsoids are tuned during implementation.

    To obtain the overall position error covariance, we combine the error covariance matrices for all the individual surface and edge feature points. Figure 7 shows the combined covariance ellipsoid for two different scenarios. We observe that while passing through a corridor, the covariance ellipsoid is larger in the direction parallel to the building sides due to a poor distribution of features.

    Figure 7. Overall position error covariance ellipsoids (black) for two point clouds (green). We combine the error ellipsoids from individual surface (red) and edge (blue) feature points.

    GPS MEASUREMENT MODEL

    We use pseudorange measurements from the GPS receiver to create the measurement model. To eliminate certain error terms, we use double-difference pseudorange measurements, which are calculated by differencing the pseudorange measurements between two satellites and between two receivers. Before proceeding to use the pseudorange measurements, we check if any of the satellites detected by the receiver are NLOS signals. We use the 3D city model mentioned earlier to detect the NLOS satellites. We use the position output generated by the lidar-3D city model matching to locate the receiver on the 3D city model.

    Next, we draw LOS vectors from the receiver to every satellite detected by the receiver and eliminate satellites whose corresponding LOS vectors intersect the 3D city model. Figure 8 shows the above implementation in an urban scenario.

    Figure 8. Elimination of NLOS satellite signals. LOS vectors are drawn to all detected satellites: SV3, SV14, SV16, SV22, SV23, SV26, SV31. The LOS vectors to satellites SV23 and SV31 intersect (red) the 3D city model and are eliminated from further calculations.

    After eliminating the NLOS satellites, we select satellites that are visible to both the user and the reference receivers to create the GPS double-difference measurement vector and its covariance. We assume that the individual pseudorange measurements are independent, and that the variance for each measurement is a function of the corresponding SNR. We propagate the covariance matrix for the individual pseudorange measurements, to obtain the covariance matrix for the double-difference measurements.
    GPS-Lidar Integration

    In addition to using a lidar and a GPS receiver, we use an IMU on board the UAV. Figure 9 shows the experimental setup: the UAV designed and built by our research group. For the double-difference GPS measurements, we use a reference receiver within a kilometer of our data collection sites. We implement a UKF to fuse measurements from the sensors and estimate the global pose of the UAV.

    Figure 9. Experimental setup for data collection. Our custom-made iBQR UAV mounted with a lidar, a GPS receiver and antennas, an IMU, and an onboard computer.

    Position and orientation estimates from lidar and GPS are incorporated via the correction step of the filter, whereas the IMU measurements are included in the prediction step. For position corrections from lidar, we use our point cloud feature based model for the error covariance. For GPS double-difference measurements, we use the covariance based on the individual pseudorange measurement SNR.

    We implement our algorithm on an urban dataset collected on our campus of University of Illinois at Urbana-Champaign. As shown in Figure 10, the GPS measurements and the GPS position output contain large errors, due to the presence of nearby urban structures. Here we stack all the double-difference measurements and compute the unweighted least square estimate of the baseline between the UAV and the reference receiver.

    Figure 10. Position estimates from GPS measurements. The position output from the GPS receiver (blue) and the unweighted least-squares position estimate (red) contain large errors.

    For the lidar measurements, we check the output from our incremental ICP odometry method and the lidar-3D city model matching algorithm. Furthermore, we implement an ICP mapping algorithm to check the performance of existing ICP-based methods on the dataset. In Figure 11, the ICP odometry method and the ICP mapping algorithm accumulate drift over the course of the trajectory. The lidar-3D city model matching algorithm does not drift over time; however, the position still contains errors in situations where the lidar does not detect enough number of points or the matching algorithm converges to a local minimum.

    Figure 11. Position estimates from lidar point clouds. The incremental ICP odometry (green) and the ICP mapping (blue) estimates accumulate drift over time. The lidar-3D city model matching (yellow) does not drift over time, but contains errors where the ICP algorithm might converge to a local minimum.

    Figure 12 shows the output of the filter for the same trajectory. The filter output estimates the actual path much more accurately than the individual measurement sources by themselves.

    Figure 12. Position estimates from UKF, integrating GPS and lidar measurements. The filter position output (blue) resembles the actual trajectory, more accurately than any individual source of GPS or lidar measurements.

    CONCLUSION

    In summary, we proposed a GPS-lidar integration approach for estimating the navigation solution of UAVs in urban environments. We used the onboard lidar point clouds in two ways: to estimate the odometry by matching consecutive point clouds, and to estimate the global pose by matching with an external 3D city model. We built a model for the error covariance of the lidar-based position estimates as a function of surface and edge feature points in the point cloud. For GPS measurements, we eliminated NLOS satellites using the 3D city model and used the remaining double-difference measurements between an onboard receiver and a reference receiver. To construct the covariance matrix for the double-difference measurements, we used the SNR values for individual pseudorange measurements.

    Finally, we applied an UKF to integrate the measurements from lidar, GPS and an IMU. We experimentally demonstrated the improved positioning accuracy of our filter.

    ACKNOWLEDGMENTS

    The authors would like to sincerely thank Kalmanje Krishnakumar and his group at NASA Ames Research Center for supporting this work under the grant NNX17AC13G.
    The material in this article was first presented at the ION GNSS+ 2017 conference in Portland, September 2017.

    MANUFACTURERS

    The lidar used aboard the UAV in these tests is a Velodyne VLP-16 Puck Lite. The GPS receiver is a u-blox LEA-6T with a Maxtena M1227HCT-A2-SMA antenna. The IMU is an Xsens Mti-30, and the onboard computer an AscTec Mastermind 3a.

    The iBQR UAV was designed and assembled by the authors.

    AKSHAY SHETTY received an M.S. degree in aerospace engineering from University of Illinois at Urbana-Champaign. He is also pursuing a Ph.D. at the same university.

    GRACE XINGXIN GAO received a Ph.D. degree in electrical engineering from Stanford University. She is an assistant professor in the Aerospace Engineering Department at the University of Illinois at Urbana-Champaign.

  • Systems Engineering Group to demonstrate UAV tech at naval event

    Telephonics Corporation’s subsidiary, Systems Engineering Group (SEG), will demonstrate autonomous UAV control and PULSEbox this month during the Annual Naval Technology Exercise (ANTX), Dahlgren (Virginia) Division event.

    ANTX-Dahlgren, being held Sept. 13-14, is a two-day event providing a low-risk environment to evaluate technological innovations at the research and development level before technologies become militarized and integrated at the operational level.

    The autonomous UAV control demonstration will include a system manager in a UAV control ConOps scenario. System Manager is a model-based expert system of systems, which can plan, schedule and initiate ConOps processes to provide round the clock automation in the Flight Dynamics Operations Area (FDOA) on NASA’s Magnetospheric Multiscale (MMS) mission. This enables NASA to minimize human involvement in controlling satellite maneuvers along with optimizing data downloads.

    PULSEbox offers a high fidelity, real-time, RF threat scene generator that integrates SEG’s threat models with optimized hardware. The system will create advanced test ecosystems by providing real-world target simulated threat states and related radar representations in laboratory settings, leading to improved testing of interoperable elements before live-sea testing events of air-breathing and ballistic missile threats, the company said.

    “Both the autonomous UAV control and PULSEbox technologies align with the U.S. Navy’s requirements for more autonomous systems with limited human control requirements and more realistic training, simulation and modeling environments,” said Michael Anderson, Telephonics vice president and SEG general manager.

  • Remote Geosystems UAV software free for hurricane work

    To assist with Hurricane Harvey and Irma emergency response and damage assessments efforts, Remote GeoSystems is donating LineVision software licenses to official agency, volunteer and non-profit drone operators.

    In addition to supporting a Texas A&M team responding to Harvey, LineVision is being pre-deployed to volunteers organized by Florida State University’s Emergency Management and Homeland Security Program to help with the Hurricane Irma search and rescue and damage assessment.

    Any other volunteer teams, first responders and non-profit organizations providing essential response and recovery services are encouraged to complete the contact form to request free copies of LineVision software for disaster relief efforts.

    LineVision lets emergency response teams easily map drone video of Hurricane Harvey damage assessments. (Image: Remote Geosystems)
    LineVision lets emergency response teams easily map drone video of Hurricane Harvey damage assessments. (Image: Remote Geosystems)

    The LineVision solution is a commercial software suite for UAV, airborne and terrestrial mobile inspection and survey projects requiring geo-referenced video playback, analysis, collaboration and reporting using standard Esri maps and data, Esri ArcMap and Google Earth GIS applications.

    Using the software, anyone with a GPS-enabled video camera, drone or geospatial DVR that can geotag video in the proper format can immediately load their videos and photos to Esri ArcGIS and Google Earth along with compatible geospatial data.

    As the video plays, a position marker moves along an aerial or terrestrial GPS track positioned on a map, continuously indicating where the current frames were recorded. Users may also geospatially “navigate” a video recording by simply clicking a single point along an aerial or terrestrial GPS track.

    The video then automatically advances to that point in the recording so that users can visually interpret what was recorded at that specific place and time. If something of interest is detected in the video, users may also “snap” an image from the video, which is geotagged and saved for future analysis.

    In addition to video, users can import photos and documents from disaster survey and assessment projects. All these imported data types can be saved in a Remote GeoSystems “geoProject” file for data portability, reporting and future analysis in other versions of LineVision desktop, cloud and server applications.

    Help with Harvey

    Remote GeoSystems was contacted by the Texas A&M Engineering Experiment Station Center for Robot-Assisted Search and Rescue (CRASAR), who was deployed with the Fort Bend County Office of Emergency Management.

    All parties involved moved quickly, and within a few hours after being contacted, drone video data collection teams were using various versions of the company’s donated LineVision video and photo mapping software to map and view interactive UAV flight tracks with corresponding videos in Esri ArcGIS and Google Earth GIS software.

    The software is being used to help visualize, distribute and share the data available from a record 119 UAS flights that CRASAR conducted over 11 days, including 61 flights on a single day.

    “We first learned about Remote GeoSystems’ LineVision software for mapping geotagged video from drones about a year ago, and at that time even did a proof of concept demo for the USCG and first responders,” said Justin Adams, Air Operations Branch Director for Fort Bend County Manned/Unmanned Ops and CRASAR director of operations for Harvey.  “Now with the Texas Gulf Coast facing a long and difficult assessment and recovery process and Hurricane Irma bearing down on Florida, it became clear now was the time to deploy this valuable UAV solution to operators and volunteers working the affected areas.

    “I have been involved in manned and unmanned aviation for the better part of two decades and Remote Geo offers not only the simplest, but most complete solution for rapid geospatial aerial and ground-based disaster assessment and reporting in the industry.”

    Key Features of LineVision

    • Play videos from single and multi-camera data collection platforms
    • “Click-on-Map” video navigation
    • Set a custom geo-fence around the moving position marker
    • Load Esri ArcGIS or Google Earth-compatible geospatial data files
    • Save video and photo work as geoProjects for simple project reporting, archive and search
  • Taoglas launches comprehensive range of high-precision GNSS antennas

    Taoglas launches comprehensive range of high-precision GNSS antennas

    The BOLT A.90.A.10451111. (Image: Taoglas)

    Taoglas, a provider of IoT and M2M antenna products, has launched a range of high-performance GNSS antennas specifically designed to power the next generation of applications that require highly accurate location capabilities.

    These applications include navigation, unmanned aerial vehicles (UAVs), surveying, agriculture, connected cars and autonomous vehicles.

    The new antenna range is Taoglas’ most comprehensive series of high-precision GNSS antennas and incorporates new form factors and use of multiple RF bands.

    Taoglas’ new range includes systems and antennas that use Galileo, GLONASS and BeiDou, as well as GPS L2 or L5 bands.

    “Today’s connected devices and applications demand new ways of approaching the age-old problem of location accuracy,” said Dermot O’Shea, co-CEO for Taoglas. “In certain applications, there is simply no room for positioning errors — location accuracy is an absolute requirement.”

    The GRS.10 smart antenna. (Image: Taoglas)

    The new antenna range includes:

    • The GRS.10, a smart antenna that includes a high-performance Taoglas GNSS (GPS, GLONASS, Galileo, BeiDou) ceramic patch antenna module integrated with a u-blox NEO-M8U GNSS receiver.
    • The Torpedo series GNSS quadrifilar helical antennas, extremely high-performance wideband satellite antennas for position-information-critical applications. It provides high circularly polarized antenna gain across a wide beamwidth. These are available in a passive (QHA) or active (AQHA) versions.
    • The BOLT A.90.A.10451111, a new GNSS timing antenna that includes lightning-induced surge protection. It is designed for the base station market. The advantage over other timing antennas is the addition of GLONASS and BeiDou frequencies.

    The complete range of precision GNSS antennas also includes:

    • The MAT.12A. (Image: Taoglas)

      The ASFGP.36A.07.0100C, a ceramic GPS L1/L2 low-profile, low-axial-ratio, embedded stacked active patch antenna.

    • The MAT.12A, a GPS/GLONASS/BeiDou dueling-loop chip antenna evaluation board, which delivers the advantages of a circularly polarized patch antenna with two miniaturized low-profile chip antennas on a smaller PCB footprint at one-fifth the weight.

    This week, Taoglas also launched small form-factor ultra-wideband (UWB) antennas designed to work with DecaWave’s chipset and module solutions for applications including asset tracking, follow-me drones, healthcare monitoring, smart home services and other applications that demand high-performance indoor localization capabilities.

    Taoglas’ complete range of GNSS and UWB antennas will be on display in Booth N.614 at Mobile World Congress Americas, Sept. 12-14, in San Francisco.

  • Drones a valuable tool in hurricane recovery efforts

    Hurricane Harvey is the first major catastrophe in which drones have been used on a large scale by both government and commercial operators, said Ken Long, an analyst at the Freedonia Group.

    UAVs are also likely to find widespread use if Hurricane Irma either directly strikes or skirts the east coast of Florida early next week, as current projections show.

    In addition to helping keep emergency workers safe by allowing them to look for people trapped by floodwaters and inspect damage in high-risk areas, drone use can speed up the recovery process. Drones can be flown over structures such as fuel tanks, power lines and railroad tracks before they can be reached by land, enabling government agencies and utilities to identify what is in most urgent need of repair.

    They also allow insurance adjusters to more quickly process claims, enabling rebuilding efforts to get underway faster. Farmers Insurance reports that an insurance inspector using a drone can complete up to eight times the number of home inspections each day than he or she otherwise would be able to do.

    When Hurricane Harvey first made landfall in Texas on Aug. 25, the Federal Aviation Administration (FAA) set up a temporary but extensive no-fly zone over Houston and nearby areas to help protect first responders in helicopters and other manned aircraft. This flight ban included all drone operations except those specifically approved by the FAA.

    https://youtu.be/XRdUV4WqnDE

    In the 10 days that followed Hurricane Harvey, the FAA issued more than 100 separate authorizations for drone use in the Houston area, according to the Wall Street Journal. Some of the applications for drone use were reviewed and approved by the FAA within hours, an unusually fast turnaround time for an agency that typically takes days or weeks to make decisions.

    With the exception of a handful of flights conducted by media firms, all of the approved operations were for drones used in conjunction with, or on behalf of, government agencies. Drones were used to inspect bridges, roadways and power lines; assess the condition of oil refineries and water plants; and survey coastal damage.

    As the flood waters continued to recede and flight restrictions were eased or lifted, insurance companies — including Allstate, Farmers Insurance, Travelers and USAA — began to use drones to assess property damage and speed claims processing.

    However, drone use by insurance companies and other commercial users is limited by FAA rules that do not allow them to be flown above 400 feet, outside the visual line of sight of the operator, or above people not directly involved in their operation, unless a waiver is granted.

    These regulations could change with a 2018 FAA reauthorization bill being considered by Congress.

    “The demonstrated usefulness of drones in Hurricane Harvey response and recovery efforts could well influence the content of that legislation,” Long said.

    Even if the current FAA regulations remain in place, U.S. commercial drone demand will expand rapidly from what is currently an extremely small market base, according to the Freedonia Group’s Drones (UAVs) study. “Non-military government use of drones will also climb at a robust rate through 2020,” Long said.

    Both commercial and non-military government market gains will be fueled by further improvements in drone designs, making them more capable and easier to operate, customized for use in specific applications and cost-saving.

  • Honeywell teams with Intel on UAV inspection service

    Honeywell teams with Intel on UAV inspection service

    Honeywell has launched its first commercial unmanned aerial vehicle (UAV) inspection service — the Honeywell InView inspection service — to help industrial customers improve critical structure inspections while helping increase employees’ safety from many of the risks associated with these often-dangerous working conditions.

    Intel Falcon 8+ octocopter drone.

    The Honeywell InView inspection service will combine the proven performance of the Intel Falcon 8+ UAV system and Honeywell’s expertise in the aerospace and industrial industries with data-driven software customized to the needs of the utility, energy, infrastructure, and oil and gas industries, the company said.

    The Honeywell InView inspection service package, which includes the components of the UAV, pilot app and customizable web portal, helps customers organize and create standards around their routine and crisis-response inspections.

    For example, the Honeywell InView inspection service can help utility customers create routine inspections of transmission and distribution systems that generate data that can be stored, searched and accessed from in the office and out in the field on demand.

    “This collaboration combines Intel’s advanced commercial Intel Falcon 8+ UAV system with Honeywell’s leadership in aerospace safety and connectivity to deliver solutions that deliver reliable, efficient and actionable information to utility and industrial customers,” said Carl Esposito, president, Electronic Solutions, Honeywell Aerospace. “Through our extensive industrial experience, our customers will also gain access to Honeywell’s customized software and data solutions that will help them log, analyze, and eventually predict or prevent outages and structural failures, while protecting the men and women called upon to complete these crucial but high-risk jobs.”

    “We are incredibly pleased to collaborate with Honeywell on this exciting new business opportunity,” said Anil Nanduri, general manager for Intel’s UAV business group. “The safety, flight precision and robust performance of the Intel Falcon 8+ system are a perfect fit for the Honeywell InView inspection service and will allow its customers to inspect, collect and analyze valuable data in a whole new way.”

    With Honeywell’s InView inspection service, customers tap into Honeywell’s experience across vertical segments such as utilities, aerospace, connected building management, and oil and gas technologies.

    In collaboration with Intel, Honeywell will utilize the intelligence and experiences of its diverse set of businesses to give customers a comprehensive solution and experience unrivaled in the marketplace.

    “Technology, along with the Internet of Things, is enabling utilities around the world to modernize the management of their energy grids,” said Nitin S. Kulkarni, president, Smart Energy, Honeywell Home and Building Technologies. ” Honeywell brings together the technology that allows utilities to transform how energy is consumed in homes and buildings with software-based systems that help safely and efficiently manage complex industrial facilities and utility grids. Honeywell also has more than 100 years of experience providing dependable products and services to a variety of industries, of which Honeywell InView inspection service is the latest entry.”

    Inspection Service goals

    Keeping workers safe. According to the U.S. Department of Labor, utility line workers have one of the top 10 most dangerous jobs in the United States, with 21.5 annual fatalities from high-voltage lines for every 100,000 workers.

    By using the inspection service, utility companies can send a UAV to perform routine inspections of substations, transmission towers and power lines while keeping boots on the ground and workers safe.

    For utilities, using a UAV for inspections offers safer and more cost-effective means than existing methods using helicopters, cherry pickers, ladders and walking inspections.

    Improving efficiency. Historically, inspections are siloed by organization and by individuals within organizations. Honeywell’s InView inspection service aims to create standardized inspections where customers can create operational efficiencies in the office and out in the field.

    Data capture and analysis. UAVs are being touted for their data-gathering capabilities, but without analytics, more data is simply more data. Honeywell’s service can synthesize vast quantities of data to identify only what is needed and actionable, translating workers’ tacit knowledge into valuable information that provides actionable insights for business.

    Connected Freight

    Honeywell and Intel also recently collaborated to create a Connected Freight platform that gives shippers and logistics companies the unprecedented ability to monitor shipments of high value and perishable goods, helping prevent costly damage and loss.

    The new Honeywell InView inspection service continues the work these two companies are doing to help various industries use connected devices to be more efficient and safer, and harness data in new and meaningful ways.

     

  • Can artificial intelligence fly a drone? Researchers are finding out

    Can artificial intelligence fly a drone? Can a drone catch thermals the way birds do?

    Microsoft researchers are partnering with the Nevada Governor’s Office of Economic Development (GOED) and the Nevada Institute for Autonomous Systems (NIAS) to find out.

    The artificially intelligent UAS being tested at the Nevada UAS Test Site is a 16 ½ -foot, 12 ½- pound sailplane. The sailplane relies on a battery to run onboard computational equipment and controls such as the rudder, plus radios to communicate with the ground.

    It also has a motor so that a pilot can take over manual operation when necessary.

    But once it’s up in the air, the UAS demonstrated its ability to operate on its own, finding and using thermals to travel without the aid of the motor or a person.

    Simple and complex UAS testing was conducted at the Hawthorne Advanced Drone Multiplex (HADM) Test Range located at Hawthorne, Nevada. HADM is a 230-square mile area where a variety of UAS applications can be tested, including artificial intelligence (AI).

    NIAS manages the FAA-designated Nevada UAS Test Site, which includes HADM and other UAS test ranges across Nevada.

    The Microsoft operation was based at the Hawthorne Industrial Airport where preliminary tests were made. Subsequent tests were conducted at an area east of Walker Lake around six miles from the airport.

    The team flew three different sailplanes that reached an altitude of approximately 1,700 feet flying almost two dozen Nevada UAS Test Site Certification of Authorization (COA) flights Aug. 7-11.

    “Innovative AI technology like what Microsoft tested with NIAS is clearly where the most dramatic global UAS Industry disruptions will occur,” said Chris Walach, test site director. “When you think of artificial intelligence or AI, there are many perspectives on the value-add to the UAS industry. Very evident to me, developing and testing AI, or machine learning technology, is going to have multiple applications that will significantly benefit the UAS Industry and the American way of life. This is one of the most exciting developments I have seen over the past several years in Nevada and globally.”

    “Microsoft researchers have created a system that uses artificial intelligence to keep the sailplane in the air without using a motor, by autonomously finding and catching rides on naturally occurring thermals, like how wild birds stay aloft,” said Ashish Kapoor, a principal Microsoft researcher. “Birds do this seamlessly, and all they’re doing is harnessing nature and they do it with a peanut-sized brain.”

    “Nevada wholeheartedly supports the growth of the Unmanned Aerial System industry, and teaming with global technology leader Microsoft to perform these Nevada-based tests speaks to our leadership role with the global community,” said Tom Wilczek, industry specialist for the Nevada Aerospace and Defense Industry for the Governor’s Office of Economic Development. “Governor Sandoval and our Legislature expect us to engage in the growth of transformative technologies and I am grateful for the opportunity afforded by Microsoft to team and to do just that.”

     

  • Nearmap expert joins Aug. 31 mapping and UAV webinar

    Tony Agresta of Nearmap has joined the panel of speakers on the Aug. 31 webinar, “Integrated Technologies for Industrial Positioning.”

    Tony Agresta, Nearmap

    The webinar is free (register here) and focuses on applications in the electric utility/telecom sector, such as site inspections, drones and geographic information systems (GIS) mapping in general. Participants will learn how to maximize reach and capabilities using various sensors and technologies integrated with GPS aboard unmanned autonomous vehicle (UAV) platforms.

    Agresta leads the U.S. marketing effort including customer use cases for Nearmap across industries.

    Nearmap provides instant access to high resolution aerial imagery including ortho, oblique and now 3D — at scale. Today, this imagery is used for site locate analysis, planning and tracking change over time. The webinar presentation will review the different forms of imagery, how they are captured, managed and delivered in the cloud and used inside ESRI and AutoDesk.

    Nearmap provides cloud-based subscription access to up-to-date 2-D orthomosaic aerial imagery. Using its patented HyperCamera2 technology, Nearmap is applying the same access model to the oblique aerial imagery market.

    Screen capture from a Nearmap 3D fly-through of Austin, Texas, rendered from Nearmap oblique Imagery.

    Because this new camera system provides a high degree of overlap from different angles, Nearmap can reconstruct the real world in stunning detail, producing not only high-resolution orthomosaic and oblique imagery, but also surface and terrain models, natural-color point clouds and textured 3-D meshes.

    Other Speakers on the Panel

    Jeff Fagerman, Lidar USA

    Jeff Fagerman. Fagerman, a professional surveyor and certified photogrammetrist, is founder and owner of Lidar USA. He holds a master’s degree in photogrammetry from Purdue University. During his tenure with Intergraph from 1985 to 1999, he worked as a photogrammetric software developer on that company’s innovative photogrammetric workstations. In 1999, he started Fagerman Technologies, now known as Lidar USA. In 2010, the main corporate focus became mobile lidar aboard UAVs.

    Lidar USA provides solutions for GIS, surveying, civil engineering, agriculture, forensics, BIM, heritage mapping — all things 3D and beyond. In addition to UAV-based mapping and surveying, the company has developed ground—based lidar, building an economical mobile mapping system called ScanLook, incorporating scanning, imaging, and navigation. The company has provided client services in survey/mapping, agriculture, law enforcement, military, archaeology, and education.

    Chris Lund, Honeywell

    Chris Lund, Honeywell Corporation. Lund will focus on inertial sensors as the centerpiece of any robust industrial positioning solution. Given they can’t be interfered with, inertial sensors are the glue that binds the information from all the other sensors together to reveal the desired insights and maximize operator uptime/efficiency.

    Lund is a senior director of product marketing for Honeywell’s Navigation and Sensor business. He has experience running product lines for inertial measurement units as well as for surface and marine navigators. Previously, he had engineering roles as a researcher, project lead and technical manager. Lund has an M.S. in the management of technology. He has been working on navigation-related technologies since the late 90s, holds multiple patents, and has co-authored several conference papers and presentations.

    Derrick Reish, LTI

    Derrick Reish, Laser Technology Inc. (LTI). (LTI) started working with the U.S. government more than 30 years ago by designing lasers that measured distances between two planes in-flight for a de-icing exercise. The company then won a contract with NASA to build a custom laser that could measure both distances and speeds for space docking missions. Its first professional measurement device was a hand-held reflector-less total station launched the GPS laser offset sector.

    LTI addresses real world needs and applications, including forestry, mining, utilities and surveying, among others. The company focuses on facilitating data collection and GPS/GNSS mapping for professionals, with innovative solutions aboard Android and UAV platforms.

    Register here for the free Aug. 31 webinar.

  • Lidar/UAV and inertial experts join panel on free webinar: Integrated tech

    Jeff Fagerman, Lidar USA

    Jeff Fagerman, a professional surveyor and certified photogrammetrist, has joined the panel of speakers on the Aug. 31 webinar, “Integrated Technologies for Industrial Positioning.” The webinar is free (register here) and focuses on applications in the electric utility/telecom sector, such as site inspections, drones and geographic information systems (GIS) mapping in general. Participants will learn how to maximize reach and capabilities using various sensors and technologies integrated with GPS aboard unmanned autonomous vehicle (UAV) platforms.

    Also joining the panel for the Aug. 31 webinar is Chris Lund from Honeywell Corporation. He will focus on inertial sensors as the centerpiece of any robust industrial positioning solution.  Given they can’t be interfered with, inertial sensors are the glue that binds the information from all the other sensors together to reveal the desired insights and maximize operator uptime/efficiency.

    The two new speakers join Derrick Reish of Laser Technology, Inc., for the webinar.

    Fagerman is founder and owner of Lidar USA. He holds a Master’s degree in photogrammetry from Purdue University. During his tenure with Intergraph from 1985 to 1999, he worked as a photogrammetric software developer on that company’s innovative photogrammetric workstations. In 1999, he started Fagerman Technologies, now known as Lidar USA. In 2010, the main corporate focus became mobile lidar aboard UAVs.

    Chris Lund, Honeywell

    Chris Lund is a senior director of product marketing for Honeywell’s Navigation and Sensor business. He has experience running product lines for inertial measurement units as well as for surface and marine navigators. Previously, he had engineering roles as a researcher, project lead and technical manager. Lund has an M.S. in the management of technology. He has been working on navigation-related technologies since the late 90s, holds multiple patents, and has co-authored several conference papers and presentations.

    Lidar USA provides solutions for GIS, surveying, civil engineering, agriculture, forensics, BIM, heritage mapping — all things 3D and beyond. In addition to UAV-based mapping and surveying, the company has developed ground—based lidar, building an economical mobile mapping system called ScanLook, incorporating scanning, imaging, and navigation. The company has provided client services in survey/mapping, agriculture, law enforcement, military, archaeology, and education.

    Derrick Reish, Laser Technology, Inc.

    Laser Technology Inc. (LTI) started working with the US government more than 30 years ago by designing lasers that measured distances between two planes in-flight for a de-icing exercise. The company then won a contract with NASA to build a custom laser that could measure both distances and speeds for space docking missions. Its first professional measurement device was a hand-held reflector-less total station launched the GPS laser offset sector.  

LTI addresses real world needs and applications, including forestry, mining, utilities and surveying, among others. The company focuses on facilitating data collection and GPS/GNSS mapping for professionals, with innovative solutions aboard Android and UAV platforms.

    Register here for the free August 31 webinar.  A final speaker expert in aerial photography  will be announced soon.

     

  • What to expect from ION GNSS+ and Intergeo 2017

    What to expect from ION GNSS+ and Intergeo 2017

    Intergeo 2016

    It’s almost September. For the GPS World staff, this means scramble time. We have two important industry events to attend: The venerable ION GNSS+ conference and the huge Intergeo trade show.

    ION GNSS+ is the Institute of Navigation’s largest technical meeting and showcase of GNSS technology, products and services. Hundreds of papers are shared by experts in the field, in presentations and panels.

    The show has changed over the years to broaden its focus to applications, and added a “+” to its name to incorporate all the positioning, navigation and timing (PNT) technology that aids GNSS in location, much as we have also done in providing a new subtitle to our magazine.

    New this year are Short Courses, aimed at bringing your non-technical staff up to speed on the technology behind the industry, no matter their background. For instance, one course is “GNSS 101: An Introduction.”

    Intergeo, which is held each year in different city in Germany, comes to Berlin. The huge show, attended by about 17,000 people, is a conference and trade fair (emphasis on trade fair) for the fields of geodesy, spatial data, surveying, UAVs and land management.

    A hot topic at Intergeo continues to be Geospatial 4.0, the massive transformation where big data, mobility and cloud solutions are driving a new global digital economy.

    Other buzzed-about topics include photogrammetry, building information modeling (BIM) and smart cities.

    One important and timely topic is the need for infrastructure that ensures data security and protection. Once again, the Interaerial Solutions show for UAVs will take place as part of Intergeo.

  • UNICEF UAS vaccine delivery trial takes place this month

    UNICEF and the Vanuatu government have selected Martek Marine to demonstrate UAV use for vaccine delivery. Martek will fund the trial, which will take place on Efate Island Aug. 21–25.

    Vanuatu is an archipelago of 83 islands —  65 of which are inhabited — that stretch 1,600 kilometers. Many islands are only accessed by boat, and mobile vaccination teams are frequently required to walk to communities carrying needed equipment to undertake vaccinations of children and communities in remote areas. The climate, lack of infrastructure and topography make this an arduous task.

    The trial aims to assess technologies and proven safe operators that can help reduce the vaccine supply chain disruption and enhance service delivery, without requiring massive investment in infrastructure and transport. It will also provide the opportunity to explore and understand the wider application and potential of UAVs in the Pacific region long term.

  • Draper equips UAVs with vision for GPS-denied navigation

    Draper equips UAVs with vision for GPS-denied navigation

    A team from Draper and the Massachusetts Institute of Technology (MIT) has developed advanced vision-aided navigation techniques for UAVs that do not rely on external infrastructure, such as GPS, detailed maps of the environment or motion capture systems.

    When a firefighter, first responder or soldier operates a small, lightweight flight vehicle inside a building, in urban canyons, underground or under the forest canopy, the GPS-denied environment presents unique navigation challenges.

    In many cases, loss of GPS signals can cause these vehicles to become inoperable and, in the worst case, unstable, potentially putting operators, bystanders and property in danger.

    Attempts have been made to close this information gap and give UAVs alternative ways to navigate their environments without GPS. But those attempts have resulted in further information gaps, especially on UAVs whose speeds can outpace the capabilities of their onboard technologies.

    For instance, scanning lidar routinely fails to achieve its location-matching with accuracy when the UAV is flying through environments that lack buildings, trees and other orienting structures.

    Finding a Solution

    DARPA awarded contracts to Draper and two other industry teams to create UAVs that autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program. (Photo: Draper)

    Working together under a contract with the Defense Advanced Research Projects Agency (DARPA), Draper and MIT created a UAV that can autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program.

    The team developed and implemented unique sensor and algorithm configurations, and has conducted time-trials and performance evaluations in indoor and outdoor venues.

    “The biggest challenge with unmanned aerial vehicles is balancing power, flight time and capability due to the weight of the technology required to power the UAVs,” said Robert Truax, senior member of technical staff at Draper. “What makes the Draper and MIT team’s approach so valuable is finding the sweet spot of a small size, weight and power for an air vehicle with limited onboard computing power to perform a complex mission completely autonomously.”

    Draper and MIT’s sensor- and camera-loaded UAV was tested in a number of environments ranging between cluttered warehouses and mixed open and tree filled outdoor environments with speeds up to 10 m/s in cluttered areas and 20 m/s in open areas.

    The UAV’s missions were composed of many challenging elements, including tree dodging followed by building entry and exit and long traverses to find a building entry point, all while maintaining precise position estimates.

    “A faster, more agile and autonomous UAV means that you’re able to quickly navigate a labyrinth of rooms, stairways and corridors or other obstacle-filled environments without a remote pilot,” said Ted Steiner, senior member of Draper’s technical staff. “Our sensing and algorithm configurations and unique monocular camera with IMU-centric navigation gives the vehicle agile maneuvering and improved reliability and safety — the capabilities most in demand by first responders, commercial users, military personnel and anyone designing and building UAVs.”

    Draper’s contribution to the DARPA FLA program — documented in a recent research paper for the 2017 IEE Aerospace Conference — was a novel approach to state estimation (the vehicle’s position, orientation and velocity) called SAMWISE — Smoothing And Mapping With Inertial State Estimation.

    SAMWISE is a fused vision and inertial navigation system that combines the advantages of both sensing approaches and accumulates error more slowly over time than either technique on its own, producing a full position, attitude and velocity state estimate throughout the vehicle trajectory.

    The result is a navigation solution that enables a UAV to retain all six degrees of freedom and allows it to fly autonomously without the use of GPS or any communication with vehicle speeds of up to 45 miles per hour.

    The team’s focus on the FLA program has been on UAVs, but advances made through the program could potentially be applied to ground, marine and underwater systems, which could be especially useful in GPS-degraded or denied environments.

    In developing the UAV, the team leveraged Draper and MIT’s expertise in autonomous path planning, machine vision, GPS-denied navigation and dynamic flight controls.