A mobile robot operating in an urban environment has to navigate around obstacles and hazards. Though a significant amount of work has been done on detecting obstacles, not much attention has been given to the detection of drop-offs and other hazards where an error could lead to disastrous consequences. In this paper, we propose algorithms for detecting obstacles and drop-offs using stereo-vision and motion cues. We propose a color segmentation stereo method and compare its performance at detecting hazards with prior work using a correlation stereo method. Furthermore, we introduce a novel drop-off detection scheme based on motion cues that adds to the performance of the stereo-vision methods. All algorithms are implemented and tested on a physical robot platform in real urban environments.

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In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-08) 2008.

@inproceedings{murarka:iros08, title={Detecting obstacles and drop-offs using stereo and motion cues for safe local motion}, author={Aniket Murarka and Mohan Sridharan and Benjamin Kuipers}, booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-08)}, url="http://www.cs.utexas.edu/users/ai-lab?murarka:iros08", year={2008} }