Loki- Direct Geopositioning in Drone Mapping · 2018. 6. 1. · 80%). When using these techniques...

AirGon LLC. 9668 Madison Blvd., Suite 202 Madison, AL 35758

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Copyright GeoCue Group, 2016

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Loki- Direct Geopositioning in Drone Mapping Lewis Graham President/CTO GeoCue Group, Inc./AirGon LLC 15 July 2017

All material in this document is copyright GeoCue Group, Inc., 2017 Executive Summary: GeoCue Group Inc., through its wholly owned subsidiary, AirGon LLC., is bringing to market Loki1, our third generation direct geopositioning system for small Unmanned Aerial Systems (sUAS or drone). Direct geopositioning reduces or, in some cases, eliminates entirely, the need for ground control points in high accuracy aerial mapping.

Loki with temporary antenna mount on Inspire 2 drone

Loki operates in Post-Processed Kinematic (PPK) mode, providing centimeter level estimates of the camera exposure stations for downstream processing by third party point cloud generation software. Via a personality cable Loki supports the DJI Phantom

1 Loki is the release name for our latest AirGon Sensor Package – Generation 3


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4 Pro, DJI Inspire 2, and any bespoke system using a camera with a flash hot shoe. At the heart of Loki is the Septentrio AsteRx-m2 Global Navigation Satellite System (GNSS) engine, the most advanced sUAS-focused system available in the market today. The AsteRx-m2 supports multiple GNSS constellations, multi-frequency and very high noise rejection. In short, it will provide excellent positional solutions in challenging environments. While the obvious Return on Investment (ROI) is the reduction in labor and complexity yielded by minimizing the need for ground control points, the real return on investment is the fact that Loki makes a low-cost drone such as the DJI Phantom 4 Pro or DJI Inspire (with X4S camera) useable in mapping applications that require survey-grade accuracy. GeoCue has solved the problem of synchronizing camera events from the Phantom and Inspire with GNSS locations to cm level precision (we have patents pending on these techniques). Loki is a “plug and play” system and is therefore easily moved from drone to drone. It adapts to different cameras via a personality cable and can therefore move to more advanced drones equipped with high end cameras. A Discussion of Accuracy: Relative accuracy (sometimes called Local Accuracy) refers to the accuracy of distance measurements. For example, when measuring a door during carpentry, you are interested in the distance between two points, not the location of the door top and bottom in some real world coordinate system. Absolute accuracy (technically the correct term is Network Accuracy) refers to knowing the location of a point relative to an external reference system/datum such as a state plane coordinate system or even a local plant reference system. A relative computation such volumetric analysis can be performed using only local accuracy if no data external to the current mission needs to be integrated into the analysis. This means both the stockpile toe and the hull (stockpile surface) are collected from the data acquired in this mission, not from a baseline data set such as toes or topographic “bottoms.” Local accuracy requires “injecting” scale into the solution. This can be achieved by ensuring that photo identifiable features of known dimension exist in the imagery (for example, by placing two ground targets and measuring the distance between). Assuming the local accuracy is adequate, an accurate volume will result. However, the location of the stockpile in a reference coordinate system will not be accurately known.

https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjjv4DZi-bKAhVB5SYKHSJfAXoQjRwIBw&url=https://www.distinctivedoors.co.uk/measuring_guide_for_doors&psig=AFQjCNEwXJM6Qsfd5PkNUh4Xn0Mod4_ryg&ust=1454949574862879


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If any external data are introduced, then good network accuracy is required and all data sets must be referenced to the same network. The simple example of this is using a reference toe collected at some past date for the base computation and the current mission point cloud data for the stockpile hull. Good Network accuracy is always required for analyzing change over time such as borrow pit computations and roadway cut and fill projects.

Network (absolute) accuracy is typically achieved using some combination of survey control and Global Navigation Satellite System (GNSS) surveying technology. Great care must be taken in managing the reference datums/coordinate systems due to the very high accuracy expectations in survey work. For example, in many locations even a difference in the geoid model between two data sets (for example,

reference data in NAVD88, Geoid 2006 but project data in NAVD88, Geoid 2012b) can result in a vertical error. One other very important consideration regarding local versus network accuracy is temporal analysis of data. If any time series analysis is anticipated, all data must be referenced to the same datum with good network accuracy. A simple example is using multiple collections to measure the change in volume of a borrow pit. Each time slice data set must be very carefully controlled (using survey control, RTK/PPK or some combination of both) to ensure that all observed differences are due to true changes in the topography as opposed to fictitious differences due to errors in the referencing of the data. In my opinion, all mine site mapping operations should be carried out with good network accuracy. This discipline allows you to approach all computations in a uniform fashion, allows newly collected data to be integrated with exiting base data and allows all data to be used in comparative (temporal) analysis.


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Modeling from Images: All methods of three-dimensional (3D) modeling from images rely on the mathematics of knowing the exact position (the X, Y, Z location) and orientation (pitch, yaw and roll) of the camera at the time each image was acquired. These six parameters are called exterior orientation (EO) in photogrammetry parlance or pose in computer vision terminology. In addition to pose, a number of intrinsic camera parameters (such as focal length, distortion parameters and so forth) must be known. These camera modeling characteristics are called interior orientation (IO) or camera calibration parameters. Traditionally, 3D data extraction from two overlapping images was (and still is) performed by stereo photogrammetric ray intersection. This allowed a user, equipped with a stereoscopic vision system (Photogrammetric workstation), to extract 3D features using manual and semiautomatic techniques. In the early 21st century, a new class of algorithms (structure from motion, SfM, and semi-global matching, SGM) emerged from robotic vision research that enables the generation of high density 3D point clouds from collections of images with a high degree of overlap between adjacent images (~60% to 80%). When using these techniques in aerial mapping, an orthographically corrected image mosaic (orthophoto) is also typically created. These new algorithms, along with miniaturization (in both size and cost) of the hardware necessary to autonomously guide drones, have enabled today’s ubiquitous drone mapping.

The interior orientation parameters of a camera are rather easily obtained from a variety of different methods ranging from laboratory calibration to in situ techniques. The determination of the exterior orientation (pose) is a bit more involved. It basically consists of finding common points in many overlapping images taken from different locations and using these common points to

compute the location of each camera station. This process is generally referred to as block bundle adjustment. It is an iterative process that begins with positional estimates of the exposure stations. These estimates are then refined using local and global error minimization techniques.

https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwiRmJz61ovVAhUK6iYKHTTPDM0QjRwIBw&url=https://www.researchgate.net/figure/283648771_fig2_Fig-2-Structure-from-Motion-requires-a-set-of-overlapping-photographs-to-reconstruct&psig=AFQjCNGtzob7l5xodfBKG2-0WcxLVRiVTw&ust=1500221510299284


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If no external information about the object space is introduced during the exterior orientation (pose) determination process, the solution is called a free net model. It will faithfully render the shape of the object being modeled but it will not have the correct scale and it will not be tied to a real-world reference system. Free net solutions are very valuable for visualization projects where measurement or positioning on a map is not important. They generally have very poor network accuracy. If the model is to have correct scale, orientation and be tied to a reference coordinate system, some external information must be introduced in the modeling process. Prior to the advent of on-board Global Navigation Satellite Systems (GNSS) positioning technology, this external reference information was introduced by placing photo-identifiable targets at known locations. In aerial mapping, these targets are called Ground Control Points (GCP). Today, GNSS receivers on board the drone can be used, often in conjunction with GCPs, to provide reference locations. Initial positional estimates: Recall from the previous section that we must find the exterior orientation (EO, pose) of each image being used in the modeling process. This is most frequently accomplished in drone mapping software packages with the Structure from Motion (SfM) algorithmic workflow. Recall that this is an iterative algorithm that converges on a solution that minimizes an error function. The SfM algorithm is usually seeded with estimates of the camera position (the X, Y, Z portion of exterior orientation) using data from the GNSS receiver being used to navigate the drone. The navigation GNSS of a drone is usually accurate to within about 5 meters planimetrically/10 meters vertically and the time of image exposure is known to within only about 1 second in systems not equipped with direct geopositioning. These errors combine to give a priori position estimates within about 10 meters of being correct. With no ground control, the SfM algorithm will usually drive this error band down to about 1 m planimetric and 2 m vertical, in ground space (e.g. the true error on the ground). Obviously, this is quite far from the accuracies needed for survey grade mapping. The solution of the EO parameters for a given set of images and object space is not unique. For example, if the initial estimates of the EO position (X, Y, Z) are far from the true locations, a local minimum solution may result that “locks in” the solution that is not the overall best solution. This means that the closer the original estimates of the EO position are to the true exposure stations, the more likely the convergent solution will


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be to the globally best solution. In other words, the better the original guess at the camera positions, the more accurate the final point cloud. Fortunately, the SfM solution is not nearly as sensitive to the initial estimates of orientation of the camera (that is, the pitch, yaw, and roll) as it is to the initial position estimates. This means little is gained in accuracy by determining high accuracy a priori estimates of orientation. It is important to note that while accuracy of the camera pose parameters is a necessary condition for accuracy of the generated models (the 3D point cloud), there is not a one to one correspondence. That is, if the position of the camera station is correct to within, say 4 cm, this does not necessary mean that the same accuracy would be measured at a ground reference point. Differential GNSS: The general principle of obtaining a position on earth by triangulating signals from orbiting satellites is pretty simple. The overall systems are referred to as Global Navigation Satellite Systems (GNSS). There are two fully operational systems in common use today; the United States Navstar Global Positioning System (GPS) and the Russian Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS). Several other systems are currently in partial operation (for example, the Chinese BeiDou system and the European Union Galileo system). Satellites whose positions are precisely know with respect to time broadcast their location via high frequency digital radio to GNSS receivers. These receivers use the encoded location and time information to determine the precise distance from the receiver antenna to the broadcasting satellites. For any particular satellite, the receiver could be anywhere on a spherical circle defined by the distance from the satellite. Three such spherical circles will intersect at two points. One point is on the earth’s surface at the location of the receiver whereas the second point is off in space. Thus, by determining this earth-bound intersection point, the receiver determines its location. So, as you can see, this is a very elegant yet simple approach to figuring out a location. Of course, the devil is in the details! The entire scheme depends on precise synchronization of time between all the satellites and the receiver. Light travels approximately 30 cm in 1 nanosecond (10-9 seconds) so high precision location requires very precise clocks! GNSS addresses this by cleverly introducing time as a fourth variable. Solving this parameter requires an additional satellite. Thus, 3D positioning


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requires the receiver to be able to “see” at least four satellites. Both GPS and GLONASS have sufficient satellites that there is no spot below about 70 degrees north or south latitude where this requirement is not met. As more satellites are added to the solution, the accuracy and reliability improves. Most modern GNSS receivers (even the very low end receivers in cell phones) support combining all seen GPS and GLONASS satellites. There are a lot of factors that affect the accuracy of GNSS signals. For example, the ionosphere causes a change in the propagation speed of the signals that is very difficult to accurately model. Other factors come in to play such as signals reflecting from objects such as trees and building, making the apparent distance farther than reality (this is the “multi-path” effect). These error sources combine to give unaided GNSS an accuracy of perhaps 5 meters in the horizontal and 10 meters in vertical. Thus, the location that you read from your cell phone when using the internal GNSS receiver will be at about this accuracy. This is nowhere near good enough for providing a priori estimates for the camera exterior orientation previously discussed. Fortunately, a clever technique called differential GNSS has been developed to greatly improve accuracy. The idea is that errors in GNSS signals at two receivers in relatively close proximity to one another (say 10 km) are very highly correlated. Using this fact, if a receiver is placed at a known location, its error can be computed and then applied to the receiver that is placed at the unknown location. The receiver placed at the known location is referred to as the base. The receiver at the unknown location is generally moving from location to location and hence is termed the rover. This technique of differential error correction results in centimeter level accuracy at the rover. You can see where this is going. By placing a small GNSS rover in a differential configuration on the drone, the a priori estimates of EO can be directly collected!

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwiY-eeAgY_VAhUE5CYKHT_bBzIQjRwIBw&url=http://www.blackroc.com/differential-gps/&psig=AFQjCNEbaLb5B1p0wjZzrA-I8mvGPQ6LwQ&ust=1500335909269448


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The differential correction can be applied in real time. This approach is termed Real Time Kinematic (RTK) differential GNSSS. Real Time because the solution is determined at the time the rover is at each position. Kinematic simple means “in motion.” A system that resolves the locations of the rover after the fact is called a Post-Processed Kinematic (PPK) system. If real time positional information is not required, the PPK approach offers a more robust implementation. A direct radio link between base and rover is not required and the positions of the satellites are known to a higher degree of accuracy as the time between collection of data and post-processing occur. The Advent of Low Cost, Global Shutter Cameras: Most low end drone cameras are optimized for videography rather than mapping. These cameras employ an exposure technique called a “rolling” shutter. You can imagine a rolling shutter as a slit that moves rapidly over the imaging sensor, building up a full frame image as a series of bands. This causes a distortion of objects that are in motions relative to the camera. Of course, in a moving drone, there is always relative motion! While corrections for the effect can be made in software, the corrections are imperfect, making cameras with rolling shutters unsuitable for sensor- in-motion mapping applications.

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwiLhvWR143VAhWCRyYKHbKZBlEQjRwIBw&url=http://www.andor.com/learning-academy/rolling-and-global-shutter-exposure-flexibility&psig=AFQjCNEpi-Dp7kFb-CNaKXj2BKFIFnaScg&ust=1500290257973026


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In late 2016, DJI released next generation cameras that include a global shutter operating mode. When operating at shutter speeds slower than 1/2000 s (which is fast enough for drone mapping), the camera’s leaf shutter provides a global operating mode. This eliminates the rolling shutter distortion problem of previous generation cameras. These cameras are the integral camera on the Phantom 4 Pro and the X4S on the Inspire 2. We performed considerable testing of the Phantom 4 Pro and the Inspire 2 in late 2016. We concluded that these models can be serious mapping platforms. The challenge was then to adopt our ASP direct geopositioning to these systems. Synchronizing the Camera: Several engineering problems needed to be solved to bring our second generation AirGon Sensor Package (ASP-2) direct geopositioning system to the new DJI drones; synchronizing the camera, shrinking the overall system size/mass, and encapsulating the system. When synchronizing a GNSS track to a camera, it is necessary to know the exact time of each exposure. A drone flying at 5 m/s advances 1 cm every 2 milliseconds (ms, 10-3 seconds). Thus, being off synchronization by only 10 ms introduces a positioning error of 5 cm! High end mapping cameras have this capability designed in. They provide an external trigger called the Mid-Exposure Pulse (MEP) that is fed to the GNSS receiver system, providing an event pulse. The positioning system detects the MEP and records the precise GNSS time of each receipt. This time tag is used in post-processing to derive the X, Y, Z location where the events occurred.

Prosumer and drone cameras do not feature the electronic circuitry for a MEP signal. Cameras that can accept an external flash unit provide a pretty easy solution to this problem. Since a flash must be synchronized to the camera’s shutter, we simply design circuitry that intercepts the flash signal and conditions it into a proper MEP. The location of attachment of a flash is called the “hot shoe” and thus this type of camera trigger event circuitry is often called a hot shoe adapter.

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjz9ubS2I3VAhUFRiYKHVWdCE4QjRwIBw&url=http://www.dji.com/zenmuse-x4s&psig=AFQjCNGl8yZc0j2NL0rBVjiM1w05fpP6Eg&ust=1500290689239635http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjXj9OC443VAhWISiYKHaBFCQcQjRwIBw&url=http://www.eos-magazine-shop.com/kaiser-hot-shoe-adapter-with-35mm-jack-c2x14427774&psig=AFQjCNHbpdyxqQUOTHfA38YZ10b1uk37Pg&ust=1500293488129459


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Cameras integral to prosumer drones (e.g. Inspire, Phantom) do not have either a MEP output signal nor a hot shoe adapter. We first looked at physical modifications to these drones; was there a point we could tie into the physical wiring of the drone? While this was certainly a feasible approach, it would require that customers send their drones to us for modification. A second approach was to monitor the control and data message traffic within the drone itself. This is the approach we have taken with Loki, our third generation direct geopositioning system. We monitor message traffic via the SD Card connection, determining the appropriate point for each MEP. We then convert this synthesized signal to the input expected by the Loki Controller. This technique of synchronizing the camera to the GNSS circuitry provides an accurate solution with no modifications to the drone. GeoCue Group has filed a patent on this (as well as a number of other techniques) for indirectly determining MEP. ASP Generation 3 GNSS Engine: With the MEP problem solved, we turned our attention to repackaging the AirGon Sensor Package, Generation 2 (ASP-2). At this same time, we surveyed the market to see if it made sense to adopt a next-generation GNSS Engine. The primary considerations in selecting the engine were:

• Low mass

• Low power consumption

• Tracking all visible satellites

• Resistance to vibration

• High noise rejection capabilities After an extensive market survey, we found the newly announced Septentrio AsteRx-m2 to be the perfect engine for Loki. This was very fortunate since we have used Septentrio technology in the two previous generations of ASP and have developed a close working relationship with this premiere producer of GNSS technology. The AsteRx-m2 features a whopping 448 hardware channels supporting multiband on GPS, GLONASS, Galileo,

MEP Connection (temporary antenna mount)


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Beidou and IRNSS2 – in other words, everything that is up there! If you are a GNSS engine aficionado, have a look at the full specifications (www.septrentio.com). You will be quite pleased with its capabilities. A fortunate side effect of selecting the AsteRx-m2 is that it is electrically compatible with the prior generation high end Septentrio GNSS engine that we use in the ASP-2. This significantly reduced risk on our surround technology design for Loki. AirGon Sensor Package, Generation 3 – Loki: ASP-3 (Loki) integrates all of the components necessary for a standalone PPK direct geopositioning system into a compact unit. The components include:

• The Loki Controller box

• Maxtena (M1227HCT-A2-SMA) L1/L2 GPS/GLONASS active GNSS antenna

• A personality cable (you select DJI or DSLR, depending on your camera)

• Controller to antenna cable

• Charging/Data cable

• ASP Software Suite

Loki is used in Post-Processed Kinematic (PPK) mode and thus a real-time correction signal between the GNSS base station and the drone is not required. A personality cable

2 We ship the Loki with both GPS and GLONASS enabled.

http://www.septrentio.com/


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adapts Loki to either a digital single lens reflex (DSLR) camera with hot shoe or to a DJI Phantom 4 Pro/Inspire 2. The user specifies the type of personality cable when ordering. This is a very flexible configuration in that you can switch between camera types by simply using a different personality cable. Loki is self-contained, using an internal LiPo battery that will support approximately 3 hours of flight time between charging. On bespoke drones, you can supply power to the Loki controller via the USB-C connector. On DJI drones, Loki automatically powers up when the drone is powered up and switches off when the drone is depowered. Loki also includes a circuit that powers down the system if it does not detect a MEP for 15 minutes. The active antenna is separate from the mounting box, providing flexibility in mounting on bespoke drones. Loki includes antenna mounting kits for the Phantom 4 Pro and for the Inspire 2. Loki Operations: Operation of the Loki is very straightforward. You can use either a virtual reference service (VRS) for the base reference or a physical base station. On a DJI drone, Loki will automatically power on and off, recording the roving GNSS signal with no user interaction required. You simply power up the drone and wait for the GNSS indicator LED to turn green (which means that the Loki GNSS has locked onto sufficient satellites to provide positioning). For systems with the Loki hot shoe adapter, the Loki unit is switched on by holding the power button for at least 2 seconds. When the flight is complete, Loki will power down automatically on DJI systems. On bespoke systems, you can manually switch off Loki by again holding the power button. If you neglect to power down the system, it will automatically switch off if it does not see a MEP for a span of 15 minutes. When the flight is complete, images are transferred off the drone (when using Loki, we recommend you do this via the drone’s USB connection) to a field computer. The data are transferred from the Loki controller to the field computer using the supplied USB-C to USB 3.0 cable and the included ASP Software Suite. The Loki battery charges anytime the USB-C cable is connected. Data Post-Processing: The Direct Geopositioning post-processing combines the raw data from the base station (or virtual reference system, VRS) and the Loki system to create a flight trajectory. The


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MEP events are then used to determine the position of each exposure station. These final exposure locations can then be exported as a comma separated values (CSV) file and/or encoded into the Exchangeable image file format (EXIF) data of the image files. The result is image geotagging information accurate to PPK level either supplied as an auxiliary file or directly encoded in the images.

Standard processing is quite simple. The data files are imported and a solve button is pressed. Advanced users can tweak various parameters but this is not necessary for normal processing. Once the image geotagging is complete, point cloud processing proceeds in the normal fashion. The output of Loki will work with any SfM software that allows you to specify the estimated accuracy of your image locations. Processing software such as DroneDeploy, Pix4D and PhotoScan Pro fully support these a priori accuracy estimates. The post processing software, ASP Software Suite, is included with the Loki system. It runs on a standard Windows PC. Does it Work?: Loki is AirGon’s third generation direct geopositioning system (ASP-3). We have performed over 500 flight using previous generation AirGon Sensor Package (ASP) systems based on prior generation Septentrio technology. These systems are routinely


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used in very high accuracy mapping applications such as differential volumetrics and high resolution topographic mapping (e.g. generating 1’ contours). These systems have been independently benchmarked against ground survey and airborne LIDAR. So yes, the technology most definitely works! Loki upgrades the ASP-2 technology in several important ways. The first is a complete repackaging, making the system useable on any drone. The second is the upgrade of the GNSS Engine to the Septentrio AsteRx-m2, dramatically improving the capabilities of an already first-class GNSS engine. Finally, the quite novel thing in Loki is our ability to interface to the DJI Phantom 4 Pro and the DJI Inspire 2 (with X4S camera). This is enabled via our innovative (and patent pending) way of detecting the times of camera exposure via a simple SD Card “plug and play” connector. We have run numerous tests with the Phantom 4 Pro and Inspire 2 with excellent results. Consider the segments of accuracy report that follow. In the below example, we flew a DJI Inspire 2 drone equipped with the DJI Zenmuse X4S camera. The Loki used in testing was the final version of all electronics and a prototype enclosure and antenna mount (neither of which impact accuracy). The flight area was a Wiregrass Construction asphalt plant where we have placed an array of surveyed ground control points. A local base station was placed over a known point, recording the base GNSS data during the duration of the project. We flew this site and processed the resultant Loki data in the AirGon Sensor Package Software Suite. We then processed the images into a three-dimensional point cloud and corresponding orthophoto using Agisoft PhotoScan Pro. The a priori estimates of EO accuracy were set to 10 cm. We used the focal length and principal point offsets from a laboratory calibration of the camera. We did not use any of the control points in the point cloud generation phase. We then brought the resultant data into GeoCue’s LP360 point cloud analysis software to measure accuracy. Nine test points were used in the accuracy analysis. All units in the tables discussed below are in meters.


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Planimetric Accuracy. This is the accuracy of the system with respect to the horizontal. You can see from the below report snippet that the mean error is 1.6 cm. The error range is 1.9 cm. Error range is one of the more important considerations when evaluating the accuracy of a system since it typically represents the random error that cannot easily be removed from the system. Mean error, on the other hand, is systematic error that might result from a simple bias such as having the incorrect value for an antenna lever arm.

This result is absolutely remarkable considering that no control whatsoever was used in processing these data. Vertical error reduction is more challenging in GNSS positioning because of the geometry of the rays that extend from the ground to the satellites. Vertical error is usually 2 to 3 times larger than horizontal error. Yet vertical error is the major contributor to errors in differential volumes and topographic mapping. The vertical results for this mission are shown below. Again, this is with no ground control introduced into the solution. Note the mean error of – 7.5 cm. This indicates a bias error (hence do not consider the Root Mean Square Error, RMSE, for this run since it folds in the bias error). In our case, it is because we have not yet done the fine vertical calibration of the antenna offset. Note the vertical error range of only 3.5 cm! Again, this is truly remarkable accuracy for no control


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The summary here is that not only does Loki work but it works remarkably well. While we would always want a bit of control present (if for nothing but to measure accuracy), these results show that Loki can be used with no control at all for a number of project types. Summary: Loki will truly revolutionize drone mapping by brining survey grade accuracy to low end drones while simultaneously maintaining our traditional support for higher end systems. Loki makes it practical and affordable to do deployments requiring a fleet of drones such as multisite mining, supporting multiple field crews at disparate operations, and similar scenarios. The Loki Controller is relatively rugged, allowing you to move the Loki from a crashed drone to a replacement. Loki is a third-generation system from a long-term player in the photogrammetric mapping industry. You can be assured that we will be here both for post-deployment support and as the source for the next generation systems. About Us: AirGon is a subsidiary of the GeoCue Group, Inc. GeoCue has been engaged in developing software tools and providing consulting services to the metric mapping industry since its founding in 2003. Prior to the founding of GeoCue, I was the CEO of a global photogrammetric hardware and software company called “Z/I Imaging3.” Z/I Imaging designed and built4 the Digital Mapping Camera (DMC), the world’s first digital large format framing metric camera. This camera was equipped with a very high end direct geopositioning system, the primary subject of this paper. Thus, our team is deeply knowledgeable with respect to creating both hardware and software for precision aerial and mobile measurement/mapping systems. In 2012 we formed AirGon LLC, focused on developing and deploying technology to enable precision measurement and mapping using small unmanned aerial systems (sUAS), also commonly known as drones. Our focus in AirGon has been on tools and techniques for aerial surveying of open pit mines, industrial plants and construction sites of a scale that makes sense for a small Unmanned Aerial System data collect. The usual products include a site orthomosaic,

3 Z/I Imaging was a joint venture between Carl Zeiss of Germany and Intergraph Corporation. It is now a part of Hexagon AB 4 Circa 2001


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volumetric analysis of stockpiles, and topography (digital contours) for excavation areas. We also have developed tools and techniques for overburden volume computations and general cut/fill (temporal differential) mapping. In 2014, AirGon developed the first generation AirGon Sensor Package (ASP-1) direct geopositioning system. Using a Global Navigation Satellite System (GNSS) engine from Septentrio combined with proprietary electronics designed by AirGon, the original ASP was deployed on an AirGon designed hexcopter mapping drone (the AV-900) aimed at high accuracy aerial surveying. In 2015, the original ASP was upgraded to a second-generation system (ASP-2) incorporating an improved AirGon onboard computer and a reduced component count. At the end of 2014 we added a field service team to perform drone mapping services for those customers who desire to gradually transition to owner/operator models by contracting out to a services provider. AirGon LLC was an early recipient of a Federal Aviation Administration section 333 waiver permitting us to commercially fly drones. We now have a number of pilots with FAA remote pilot certification under the newer Part 107 rules. In addition, we have aircraft liability insurance and our field service technicians have completed basic Mine Safety and Health Administration part 46 training. As of July 2017, we have flown over 600 drone mapping missions using a variety of platforms and techniques. All missions flown by AirGon for mapping services are flown with our ASP direct geopositioning technology. In early 2016, it became obvious that bespoke high-end drones would be supplanted by low cost consumer drones for most mapping and inspection operations. The reason for this transition was obvious; the job can be done using low cost technology so the return on investment for high end systems would be positive only for certain specialized cases. After verifying that the Phantom 4 Pro and Inspire 2 drones could be used in serous mapping operations we set out to adapt our ASP direct geopositioning to these new low end drones while still supporting the bespoke, high end solutions. The result is our new Loki third generation direct geopositioning system.

Loki- Direct Geopositioning in Drone Mapping · 2018. 6. 1. · 80%). When using these techniques...

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Transcript of Loki- Direct Geopositioning in Drone Mapping · 2018. 6. 1. · 80%). When using these techniques...