Qualitative Vision-Based Mobile Robot Navigation

We present a novel, simple algorithm for mobile robot navigation. Using a teach-replay approach, the robot is manually led along a desired path in a teaching phase, then the robot autonomously follows that path in a replay phase. The technique requires a single off-the-shelf, forward-looking camera with no calibration (including no calibration for lens distortion). Feature points are automatically detected and tracked throughout the image sequence, and the feature coordinates in the replay phase are compared with those computed previously in the teaching phase to determine the turning commands for the robot. The algorithm is entirely qualitative in nature, requiring no map of the environment, no image Jacobian, no homography, no fundamental matrix, and no assumption about a flat ground plane. Experimental results demonstrate the capability of autonomous navigation in both indoor and outdoor environments, on both flat and slanted surfaces, with dynamic occluding objects, for distances over 100 m.