Detecting obstacles and drop-offs using stereo and motion cues for safe local motion

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, e.g., sidewalk curbs, downward stairs, and other hazards where an error could lead to disastrous consequences. In this paper, we propose algorithms for detecting both obstacles and drop-offs (also called negative obstacles) in an urban setting using stereo vision and motion cues. We propose a global color segmentation stereo method and compare its performance at detecting hazards against prior work using a local correlation stereo method. Furthermore, we introduce a novel drop-off detection scheme based on visual motion cues that adds to the performance of the stereo-vision methods. All algorithms are implemented and evaluated on data obtained by driving a mobile robot in urban environments.