Identification of barriers and least cost paths for autonomous vehicle navigation using airborne LIDAR data

Abstract

In the past several years, the Defense Advanced Research Projects Agency (DARPA) has sponsored two Grand Challenges, races among autonomous ground vehicles in rural environments. These vehicles must follow a course delineated by Global Positioning System waypoints using no human guidance. Airborne LIDAR data and GIS can play a significant role in identifying barriers and least cost paths for such vehicles. Least cost paths minimize the sum of impedance across a surface. Impedance can be measured by steepness of slope, impenetrable barriers such as vegetation and buildings, fence lines and streams, or other factors deemed important to the vehicle's success at navigating the terrain. This research aims to provide accurate least cost paths for those vehicles using airborne LIDAR data. The concepts of barrier identification and least cost path generation are reviewed and forty-five least cost paths created with their performance compared to corresponding Euclidean paths. The least cost paths were found superior to the corresponding Euclidean paths in terms of impedance as they avoid barriers, follow roads and pass across relatively gentler slopes.