Probabilistic Obstacle Detection Using 2 1/2 D Terrain Maps

Abstract

Navigating unstructured environments requires reliable perception that generates an appropriate world representation. This representation must encompass all types of impediments to traversal, whether they be insurmountable obstacles, or mobility inhibitors such as soft soil. Traditionally, traversability and obstacle avoidance have represented separate capabilities with individual rangefinders dedicated to each task. This paper presents a statistical technique that, through the analysis of the underlying 21/2 D terrain map, determines the probability of an obstacle. This integrated approach eliminates the need for multiple data sources and is applicable to range data from various sources, including laser rangefinders and stereo vision. The proposed obstacle detection technique has been tested in simulated environments and under real world conditions, and these experiments revealed that it accurately identifies obstacles.