Title :
Visual collision avoidance by segmentation
Author_Institution :
Artificial Intelligence Lab., MIT, Cambridge, MA, USA
Abstract :
Visual collision avoidance involves two difficult subproblems: obstacle recognition and depth measurement. We present a class of algorithms that use particularly simple methods for each subproblem and derive a set of sufficient conditions for their proper functioning based on a set of idealizations. We then discuss and compare two different implementations of the approach on mobile robots and discuss their performance. Finally, we experimentally validate the idealizations
Keywords :
image segmentation; mobile robots; navigation; object recognition; path planning; robot vision; depth measurement; image segmentation; mobile robots; navigation; obstacle recognition; sufficient conditions; visual collision avoidance; Artificial intelligence; Cameras; Collision avoidance; Contracts; Laboratories; Petroleum; Pixel; Robots; Testing; Vehicle dynamics;
Conference_Titel :
Intelligent Robots and Systems '94. 'Advanced Robotic Systems and the Real World', IROS '94. Proceedings of the IEEE/RSJ/GI International Conference on
Conference_Location :
Munich
Print_ISBN :
0-7803-1933-8
DOI :
10.1109/IROS.1994.407486
