Observing Dynamic Urban Environment through Stereo-Vision Based Dynamic Occupancy Grid Mapping

摘要

Occupancy grid maps are popular tools of representing surrounding environments for mobile robots/ intelligent vehicles. When moving in dynamic real world, traditional occupancy grid mapping is required not only to be able to detect occupied areas, but also to be able to understand the dynamic circumstance. The paper addresses this issue by presenting a stereo-vision based framework to create dynamic occupancy grid map, for the purpose of intelligent vehicle. In the proposed framework, a sparse feature points matching and a dense stereo matching are performed in parallel for each stereo image pair. The former process is used to analyze motions of the vehicle itself and also surrounding moving objects. The latter process calculates dense disparity image, as well as U-V disparity maps applied for pixel-wise moving objects segmentation and dynamic occupancy grid mapping. Principal advantage of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. Meanwhile, compared with some existing methods, the stereo-vision based occupancy grid mapping algorithm is improved. The proposed method is verified in real datasets acquired by our platform SeT-Car.