Autonomous driving in off-road environments requires an exceptionally capable sensor system, particularly given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a complex vision system, which is able to provide the two basic sensorial capabilities needed by autonomous vehicle navigation in extreme environments: obstacle detection and path detection. A variable-width-baseline (up to 1.5 m) single-frame stereo system is used for pitch estimation and obstacle detection, whereas a decision-network approach is used to detect the drivable path by a monocular vision system. The system has been field tested on the TerraMax vehicle, which is one of the only five vehicles to complete the 2005 Defense Advanced Research Projects Agency (DARPA) Grand Challenge course.
Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision / Caraffi, Claudio; Cattani, Stefano; Grisleri, Paolo. - In: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. - ISSN 1524-9050. - 8:(2007), pp. 607-618.
Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision
CARAFFI, Claudio;CATTANI, Stefano;GRISLERI, Paolo
2007-01-01
Abstract
Autonomous driving in off-road environments requires an exceptionally capable sensor system, particularly given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a complex vision system, which is able to provide the two basic sensorial capabilities needed by autonomous vehicle navigation in extreme environments: obstacle detection and path detection. A variable-width-baseline (up to 1.5 m) single-frame stereo system is used for pitch estimation and obstacle detection, whereas a decision-network approach is used to detect the drivable path by a monocular vision system. The system has been field tested on the TerraMax vehicle, which is one of the only five vehicles to complete the 2005 Defense Advanced Research Projects Agency (DARPA) Grand Challenge course.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.