LiDAR-Based Object Tracking and Shape Estimation Using Polylines and Free-Space Information

Abstract

Reliable object perception is a vital requirement for automated driving. Despite the availability of precise contour measurements, most state-of-the-art tracking systems still represent object geometry as bounding boxes. However, there are objects operating in public traffic for which the box assumption is highly inappropriate. We therefore propose to represent object contours using 2D polylines. Taking into account the mutual dependence of object poses and shape, our tracking framework targets at a simultaneous estimation of both states. Moreover, we propose to augment scan segments with free-space information at their boundaries and show how this knowledge can be incorporated into the tracking framework and beyond. Evaluation with real scan data shows that our method produces accurate dynamic estimates and consistent shape reconstructions.