Joint perception and planning for efficient obstacle avoidance using stereo vision

Abstract

Stereo vision is commonly used for local obstacle avoidance of autonomous mobile robots: stereo images are first processed to yield a dense 3D reconstruction of the observed scene, which is then used for navigation planning. Such an approach, which we term Sequential Perception and Planning (SPP), results in significant unnecessary computations as the navigation planner only needs to explore a small part of the scene to compute the shortest obstacle-free path. In this paper, we introduce an approach to Joint Perception and Planning (JPP) using stereo vision, which performs disparity checks on demand, only as necessary while searching on a planning graph. Furthermore, obstacle checks for navigation planning do not require full 3D reconstruction: we present in this paper how obstacle queries can be decomposed into a sequence of confident positive stereo matches and confident negative stereo matches, which are significantly faster to compute than the exact depth of points. The resulting complete JPP formulation is significantly faster than SPP, while still maintaining correctness of planning. We also show how the JPP works with different planners, including search-based and sampling-based planners. We present extensive experimental results from real robot data and simulation experiments, demonstrating that the JPP requires less than 10% of the disparity computations required by SPP.