|Abstract:||Autonomous localization is the process of determining a platform’s position without the use of any prior information external to the platform using only what is available from the environment perceived through sensors. In this paper, we describe a technique using a collaborative swarm of UAVS’s with the goal of assisted navigation in GPS-denied environments. We consider two teams of UAVs, operating at different times, and operating collaboratively. The first team, equipped with LiDAR, fly over the unknown area generating a Digital Terrain Model (DTM) and Digital Surface Model (DSM). The second team, equipped with low-end passive-vision sensors, fly over the same area later using the information generated by the first team for landmark navigation. The second team operates without GPS using the terrain as a source of localization. To enable this scenario, we have developed an algorithm for terrain-aided navigation based on Point-Pixel matching to provide environment perception. We tested the Point-Pixel matching algorithm in a series of UAV flights. A UAV from the first team was flown in a 1km x 0.5km area collecting geo-referenced LiDAR point clouds. This digitized form of the map was converted to a regularly interpolated grid and treated as the reference image. Next, a UAV from the second team was flown in the same area. Pixel images were captured at 0.2 Hz, allowing us to find matches between the captured pixel-image with the reference point- image obtained from the LiDAR. Once a match was found, a spatial resection algorithm was applied, and the computed platform position was used to update the system navigation filters in a loosely-coupled manner allowing for navigation in the absence of GPS.|
Proceedings of the ION 2017 Pacific PNT Meeting
May 1 - 4, 2017
Marriott Waikiki Beach Resort & Spa
|Pages:||1120 - 1133|
|Cite this article:||
Moafipoor, Shahram, Bock, Lydia, Fayman, Jeffrey A., "Autonomous UAV Navigation based on Point-Pixel Matching," Proceedings of the ION 2017 Pacific PNT Meeting, Honolulu, Hawaii, May 2017, pp. 1120-1133.
ION Members/Non-Members: 1 Download Credit