A Riegl VZ-6000 laser scanner, operating at 1064 um wavelength, serves as the backbone of the ATLAS system.
Leigh Stearns, a geologist with the University of Kansas, is working with a Riegl VZ-6000 ultra long range terrestrial laser scanner, incorporated into an ATLAS (Autonomous Terrestrial Laser Scanning) system, to monitor rates of ice loss on the Helheim Glacier, a tidewater glacier undergoing large-scale changes due to global climate change.
“Lidar is an emerging technology for the earth sciences because it produces an incredibly detailed 3-D view of features,” said the KU researcher. “Repeat lidar scanning reveals small-scale changes with very high precision. These systems are now used to measure how bridges are sagging, how tectonic faults propagate and now how glaciers flow. The ATLAS systems are unique because they’re designed to scan the glacier terminus every six hours, year-round. That’s not a trivial task when there’s no sunlight in the winter, winds are high and it’s very cold.”
The VZ-6000 high speed, high-resolution terrestrial 3D laser scanner offers an extremely long measurement range of more than 6000 meters for topographic (static) applications. Due to its laser wavelength, it is exceptionally well suited for measuring snowy and icy terrain in glacier mapping and monitoring applications in mountainous regions.
Learn more about the project at the University of Kansas website.
The Think 3D Stormbee multicopter integrated with Trimble’s AP15 provides efficiency, accuracy and performance for lidar surveys from unmanned vehicles.
Historically, lidar-based aerial surveys were impractical for all but the largest unmanned systems. Because of Applanix’ development of small, lightweight and low-powered direct georeferencing solutions, airborne lidar scans from small drones are now practical, cost-effective, highly accurate and excellent options for lidar surveys, according to the company.
The Stormbee is a directly georeferenced UAV lidar solution for 3D industrial mapping applications, designed to collect survey grade spatial data in a significantly more cost effective and efficient way than static lidar.
The Stormbee, a Faro Focus 130 laser scanner, and the AP15.
Stormbee’s 3D mapping technologies include Faro’s Focus 130 laser scanner, Trimble’s AP15 high performance GNSS/inertial receiver, Applanix’s POSPac UAV GNSS/inertial post-processing software and Stormbee’s proprietary Beeflex software for lidar point cloud generation.
Industrial applications (GNSS-denied environments) pose unique challenges for laser scanning using traditional static systems, due to obstructions and poor signal environments. These issues lead to increased costs and operational time.
By using the high-performance Trimble AP15 with two antenna and the Applanix post-processing software (POSPac MMS) for georeferencing the lidar data, Stormbee provides an accurate real-time and post-mission solution for all motion variables.
Applanix has brought together its decades of experience in multi-frequency, multi-constellation Differential GNSS and inertial based positioning and orientation with the best in small-form factor hardware and powerful software, to produce a DG solution for professional aerial mapping on UAVs.
With a system delivering better than 5-cm accuracy (real mean squared) and high resolution, Stormbee and Applanix offer 3D detail from a platform moving at speeds up to 15 meters per second. The Stormbee leverages Applanix’s decades of experience in direct georeferencing of lidar systems to collect the most accurate 3D data.
Benefits of the system:
compact, easy-to-operate and cost-effective
centimeter-level mobile positioning accuracy for 3D mapping products
improved productivity, with optimized workflow from data capture to georeferenced point cloud generation
superior visualization: Lidar scanners provide more accurate information of structures than camera technologies
Think 3D, a Belgian company, is a 3D scanning company for many industrial applications including those in the beverage, steel, pharmaceuticals, chemicals and tank terminals industries. Think3D helps companies make changes to their installations by providing a full 3D CAD model of their installation.
Stormbee to date has proven to be effective in many industries including mining, engineering, dredging, forensics, universities and survey.
Velodyne Lidar Inc., maker of 3D vision systems for autonomous vehicles, is partnering with YellowScan to integrate its VLP-16 Puck and VLP-16 Puck LITE lidar sensors into YellowScan’s Surveyor.
The result is a turn-key and reliable lidar system for demanding UAV applications, the companies said.
Real-time lidar systems for UAVs are used around the world for industrial and scientific applications, including surveying, civil engineering, archeology and environmental science.
By combining its LiveStation app with the real-time 3D data capture capabilities of Velodyne’s VLP-16 Puck and VLP-16 Puck LITE sensors — both of which feature a 360-degree horizontal field-of-view, 100-meter range, and weigh 830 grams and 590 grams, respectively — YellowScan delivers a turn-key surveyor system that can be mounted to any drone for short-time data processing needs.
The result is a real-time in-flight lidar monitoring platform, with users able to see how the final map is being generated in real-time during the drone mission, and the basic map datasets available immediately after the mission.
“YellowScan is known for its commitment to providing reliable and easy to use sensing solutions for the UAV industry, which make the VLP-16 Puck sensors an easy choice for the Surveyor system,” said Erich Smidt, executive director, Europe, Velodyne Lidar. “The VLP-16 Pucks are some of our newest offerings, with significant effort put into reducing weight while maintaining the resolution and reliability expected of Velodyne’s industry-leading lidar sensors.”
“YellowScan Surveyor, the turn-key lidar solution integrating Velodyne’s advanced VLP-16 sensor, enables mapping professionals to do more in less time thanks to tremendously high density and accurate measurements acquired from UAVs,” said Tristan Allouis, CTO of YellowScan.
After two decades of providing the U.S. government with Geiger-mode lidar data, Harris Corporation offers high-resolution lidar data and its derived products to commercial organizations.
The data can be used for land-use planning and management, transmission-line monitoring, pipeline design and maintenance, transportation engineering and planning, urban modeling, asset management and forestry analytics.
Geiger-mode lidar offers the most accurate elevation data available, according to Harris Corporation, the only provider of Geiger-mode lidar data.
According to the company, the sensor allows for collections on a large scale, while also collecting data up to 10 times faster and at 10 times the resolution of existing linear lidar sensors.
Geiger-mode lidar provides multi-angle illumination that penetrates foliage, removes shadows and and eliminates voids.
The Riegl VQ-780i waveform processing airborne laser scanner is a high-performance, rugged, lightweight and compact airborne mapping sensor designed for ultra-wide-area mapping and high productivity.
The versatile system is designed for highly efficient data acquisition at low, mid and high altitudes, covering a variety of different airborne laser scanning applications from high-density to ultra-wide-area mapping.
The system provides clutter-free point clouds with high accuracy, excellent vertical target resolution, calibrated reflectance readings and pulse shape deviation for unsurpassed information content on each single measurement.
The Riegl VQ-1560i-DW dual wavelength waveform processing airborne lidar scanning system is for high-point-density mapping applications. The new airborne lidar scanning system offers two lidar channels of different wavelengths: green and infrared (IR).
The two wavelengths allow the acquisition of scan data of complementary information content, delivering two independent reflectance distribution maps and enhanced target characterization, one per laser wavelength.
The VQ-880-GH topo-hydrographic airborne laser scanning system has online waveform processing and full waveform recording. It is a fully integrated airborne laser scanning system for combined hydrographic and topographic surveying with an form factor with reduced height optimized for helicopter integrations.
The system is offered with an integrated and factory-calibrated high-end GNSS/IMU system and up to two cameras. The design allows flexible application of these components to meet specific requirements.
General Motors Co.’s (GM) self-driving unit, Cruise Automation, has more than doubled the size of its test fleet of robot cars in California during the past three months, a GM spokesman told Reuters.
The unit is testing vehicles in San Francisco as part of its effort to develop software capable of navigating congested and often chaotic urban environments.
GM has reported more run-ins between its self-driving cars and human-operated vehicles and bicycles. Its vehicles were involved in six minor crashes in September, all of which were caused by the other vehicle.
In the past three months, the Cruise unit has increased the number of vehicles registered for testing on California streets to 100 from the previous 30 to 40.
Lidar acquisition. GM announced Oct. 9 that it hasacquired lidar technology company Strobe. Strobe’s engineering talent joins GM’s Cruise Automation team to define and develop next-generation lidar solutions for self-driving vehicles.
In September, Cruise Automation revealed the world’s first mass-producible car designed with the redundancy and safety requirements necessary to operate without a driver. The vehicle will join Cruise’s testing fleets in San Francisco, metropolitan Phoenix and Detroit.
Lidar uses light to create high-resolution images that provide a more accurate view of the world than cameras or radar alone. As self-driving technology continues to evolve, lidar’s accuracy will play a critical role in its deployment.
LizardTech, a provider of software solutions for managing and distributing geospatial content, has been awarded a U.S. patent for the compression of lidar point clouds (US 9753124).
The patented technology provides lossless compression of point clouds captured by airborne lidar sensors or terrestrial laser scanners for easy and cost-effective processing, storage and transmission of data sets.
Point cloud data goes from staggering to manageable when lidar files are compressed to the MrSID format.
“Lidar systems capture terabytes of data containing rich information that can be difficult to exploit due to the difficulty processing such massive files,” said John Hayes, the LizardTech senior engineer who received the patent. “Our lidar compression technique allows users to maximize their return on investment in point cloud data collection.”
LizardTech developed the lidar compression technology in 2009 by leveraging the wavelet transformation algorithms used to compress satellite and aerial image data sets into MrSID formats. The point cloud compression technique was first released as a stand-alone LizardTech product called LiDAR Compressor and then integrated into GeoExpress in 2015.
GeoExpress is LizardTech’s flagship software product originally created to enable geospatial professionals to manipulate digital satellite/aerial image and losslessly compress them to industry-standard MrSID or JPEG2000 files. The addition of lidar handling gave GeoExpress the ability to natively compress lidar data to MrSID and LAZ formats with no loss of data content, saving up to 75% on storage, and time in processing files.
Lidar systems are flown extensively on aircraft and unmanned aerial vehicles to collect highly accurate measurements of terrain elevations for a variety of mapping applications.
Another form of lidar, known as terrestrial laser scanning, captures point clouds at ground level — both inside and outside of building structures — for visualization of crime scenes, re-creation of accident sites, and 3D modeling of building interiors.
“Lasers are even being mounted on earth-moving equipment at construction sites for real-time capture of grading progress so that engineering managers can make on-the-spot decisions,” said Toby Martin, vice president of development and strategy at Extensis. “Lidar compression makes this possible and is revolutionizing workflows in the architecture, engineering and construction (AEC) industry.”
The lidar compression algorithms can be licensed via the LizardTech SDK to incorporate the technology into third-party geospatial software solutions. Already, LizardTech is seeing interest in this technology from hardware sensor developers who want to place data compression capabilities at the source of collection.
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.
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.
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.
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.
I just returned from the 38th Annual Esri International User Conference (Esri UC), which is the largest gathering of GIS (geographic information systems) professionals in the U.S. No GIS event in the U.S. is close to its scale.
Every year for the past 38 years (I presume, as I’ve only attended the last 11), Esri President Jack Dangermond begins by spending time during the kick-off plenary session painting his GIS vision. I appreciate that he doesn’t just dive into Esri-product-specific information. Granted, I know he’s setting the stage for that, but why wouldn’t he? He has a vision, and the products Esri develops will naturally follow that vision. Every year during his plenary presentation, I look for striking statements he makes. This year, a statement that struck me was:
“GIS users come from nearly every field of human endeavor.”
Remember this slide from the Esri UC Plenary in 2015?
The concept was that historically, geospatial technology has been a technology for scientists, but as geospatial awareness builds with business consumers and then mainstream consumers, the users of geospatial technology will count in the millions and, eventually, billions of users. One could argue that location-based services (LBS) have already reached more than one billion as consumers use geospatial technology in their mobile phones for navigating.
Without geospatial technology, the mobile phone would just display latitude/longitude, offering no situational awareness. That’s not what the above slide is referring to. Geospatial awareness for the business consumer (and mainstream consumer) is becoming more about analytics. A communication tool, a decision-making tool. … not only for the scientist, but for a much wider audience.
Of course, some will say I’m just “drinking the Esri Kool-Aid.” I would agree, except for one point: It’s actually happening. Think about it.
Clearly, geospatial technology has reached thousands of users. (Reference the above slide.) Also, it’s clear that geospatial technology has already reached hundreds of thousands of users. We know this from market research, and even Esri has stated in the past it has about 350,000 customers of its enterprise, desktop and mobile products.
How about millions of users? Check out the following slide Mr. Dangermond presented at this year’s plenary session…
…4.4 million!
That’s more people that live in the State of Oregon (where I live). That’s more than one percent of the entire U.S. population. That’s the number of ArcGIS Online users.
If you’re still not convinced about the direction of the trend, then consider the number to the right of 4.4 million on the slide above: “+30%.” That means a 30 percent increase in ArcGIS Online users (presumably from this time last year). If you look closely at the slide, you’ll see that 30 percent is the lowest number. Map tiles served increased 95 percent to 3 billion. Open data downloads were more than 40 million, an increase of 200 percent.
Esri is a fascinating business case. With any other business model, it would be very difficult to accomplish what Esri has. Three points stand out to me:
Esri has remained a privately held company. In other words, they didn’t “go public” and risk polluting its culture. Also, being a privately held company held means Esri can make major strategic decisions (such as shifting to web GIS) very quickly without having to worry about Wall Street or the next quarter’s financial report. This is very rare, and makes it very difficult for other companies to compete with Esri. Esri says it spends 28 percent of its revenue on R&D (research and development). In comparison, Microsoft spends 13 percent.
The key management team has stayed intact. Senior management turnover is a killer in the technology world. Every time a key strategic manager changes, a company, or portion of it, is paralyzed until the next senior manager gears up. Six to 12 months can be lost during this transition. That’s an eternity in tech.
Focus. This is a function of leadership and a stable management team. Esri isn’t perfect, but they’ve done a solid job for being a billion-dollar organization.
Ok, enough of my armchair quarterbacking. Following are some quick observations.
Mobile GIS is king
The Collector and Survey123 user base is expanding, fueled by the rapid adoption of iOS and Android devices as field data-collection tools. Add to that the growth of high-accuracy GNSS receivers for the GIS professional.
This is a perfect storm of technology convergence that’s resulting in a paradigm shift in high-accuracy GIS data collection. In other words, there’s a ton of demand for iOS/Android mobile devices running hardware-agnostic data collection software (such as Collector or Survey123) connected to a high-accuracy Bluetooth GNSS receiver.
UAVs
The UAV technical sessions were jammed with people. If you’ve kept up with my GSS Monthly newsletter the past couple of years, you can see why. You can use an inexpensive UAV (~$1,500) to generate centimeter-level orthophotos, 3D models, volume calculations and elevation contours.
UAVs are another tool in the box, and one that I think most GIS users will eventually have access to. UAVs will continue to get cheaper and better. The challenge will continue to be how to consume UAV data efficiently into your GIS workflow.
Structure from motion
I see this technique being implemented with many technologies like UAVs and other devices. If you haven’t looked at the GeoSLAM device, the Zeb Revo, it looks incredible. With it, the GeoSLAM team scanned the San Diego Convention Center in 2 hours at 1.5-centimeter resolution.
The handheld Zeb Revo by GeoSLAM.Using the Zeb Revo, the GeoSLAM team scanned the San Diego Convention Center to 1.5-centimeter resolution in two hours.
The user simply walks around with it as it scans an area. No tripods, no setups. Just walk. It’s expensive, but so were GPS, UAVs and 3D scanners when they first entered the market. The beauty of the GeoSLAM product is its simplicity. Check out this three-minute YouTube video:
BYOD GNSS receivers
The transformation is here. Trimble is finally on board with the Catalyst, in a big way. No more proprietary GNSS handhelds. You pick the device you want to use (an Android smartphone or tablet) and the software you want to use, then select the BYOD GNSS receiver (submeter, decimeter, centimeter) you want to use. This is the way it is supposed to be. If you think about it, it was backwards for so many years!
Oh, and I forgot to mention. At nearly 18,000 attendees (that’s the high number I heard), this was the largest Esri UC in history. As someone who has attended the past 11 Esri UCs, this was the best one yet because I could feel the technology (hardware and software) really starting to come together to form practical solutions that can be deployed in a large scale.
Thanks, and see you next time. Follow me on Twitter.
Coastal Zone Mapping and Imaging Lidar System (CZMIL) to be shared at conferences as a critical rapid environmental assessment tool for both natural and manmade disasters
Teledyne Optech’s Coastal Zone Mapping and Imaging Lidar (CZMIL) system is a critical rapid environmental assessment tool for monitoring natural and man-made disasters. From detecting sewage pipe leaks, mapping oil slicks and measuring coastline changes after hurricanes, to counting underwater debris in the Great Pacific Garbage Patch, CZMIL excels at identifying and monitoring oceanic environmental changes, especially in emergency scenarios.
At the Oceans ’17 MTS/IEEE conference in Aberdeen, Scotland, Senior Scientist Viktor Feygels will present “CZMIL as a Rapid Environmental Disaster Response Tool.” Using case studies from CZMIL and its predecessor systems, Feygels will describe four distinct applications of Teledyne Optech lidar bathymeters. Attendees can catch this presentation in Room 15 on June 21 at 12:10 p.m.
Research Scientist Hieu Duong and Marine Business Manager Bob Marthouse will present “Small-Object Detection using Coastal Zone Mapping and Imaging Lidar (CZMIL)” at the Teledyne CARIS International User Group Conference in Ottawa, Canada. Conference attendees can hear about these applications on Thursday, June 22, 10:05 am, in the Rideau Room.
“CZMIL has proved to be ideally suited for rapid environmental assessment and small-object detection,” said Bob Marthouse. “Both the upcoming MTS/IEEE Oceans ‘17 conference and the recent United Nations Ocean Conference during the week of June 5 underline the urgent requirement to more critically monitor our oceans and coastlines. At Teledyne Optech, we were pleased to be part of this ongoing effort.”
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