The DARPA PerceptOR evaluation experiments
Autonomous Robots
A Robust Video-Based Algorithm for Detecting Snow Movement in Traffic Scenes
Journal of Signal Processing Systems
A multi-range vision strategy for autonomous offroad navigation
RA '07 Proceedings of the 13th IASTED International Conference on Robotics and Applications
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain
International Journal of Robotics Research
Self-learning classification of radar features for scene understanding
Robotics and Autonomous Systems
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Autonomous off-road navigation through forested areas is particularly challenging when there exists a mixture of densely distributed thin and thick trees. To make progress through a dense forest, the robot must decide which trees it can push over and which trees it must circumvent. This paper describes a stereo-based tree traversability algorithm implemented and tested on a robotic vehicle under the DARPA PerceptOR program. Edge detection is applied to the left view of the stereo pair to extract long and vertical edge contours. A search step matches anti-parallel line pairs that correspond to the boundaries of individual trees. Stereo ranging is performed and the range data within trunk fragments are averaged. The diameters of eachtree is then estimated, based on the average range to the tree, the focal length of the camera, and the distance in pixels between matched contour lines. We use the estimated tree diameters to construct a tree traversability image used in generating a terrain map. In stationary experiments, the average error in estimating the diameter of thirty mature tree trunks (having diameters ranging from 10-65cm and a distance from the cameras ranging from 2.5-30 meters) was less than 5 cm. Tree traversability results from the daytime for short baseline (9cm) and wide baseline (30cm) stereo are presented. Results from nighttime using wide baseline (33.5cm) thermal infrared stereo are also presented.