Obstacle detection by direct estimation of multiple motion and scene structure from a moving stereo rig

In the context of real-time collision-obstacle avoidance this paper investigates a direct method of estimating dense scene structure and image motion. Our proposed method combines both stereo vision (involving geometric constraints) and optical flow (involving photometric constraints). It is applied to multiple motion images (i.e., images with vehicle ego-motion plus obstacle motion). Modifications to the direct method were introduced to improve the reliability of the motion estimation. The multiple-motion-estimation scheme comprises local motion estimations followed by a classification of these estimates in a simplified motion parameter space through cluster analysis. The classification enables segmentation of the different motions that are used to estimate more accurately the dense structure and motion of objects in a scene. Experimental results on both synthetic and real image sequences demonstrate the potential of the method.