Exploiting temporal coherence in scene analysis for autonomous navigation

A technique for building reliable scene descriptions by evaluating the temporal stability of detected objects is presented. The approach is designed to avoid mistakes and to increase the competence of the sensing system by tracking objects from image to image and evaluating the stability of their descriptions over time. Since the information available about an object can change significantly over time, the authors introduce the idea of a representation space, which is a lattice of representations progressing from crude blob descriptions to complete semantic models, such as bush, rock, and tree. One of these representations is associated with an object only after the object has been described multiple times in the representation and the parameters of the representation are stable. The authors define stability in a statistical sense enhanced by a set of explanations describing valid reasons for deviations from expected measurements. To illustrate the power of these ideas, the authors have implemented a system, called TraX, that constructs and refines models of outdoor objects detected in sequences of range data.