Non-linear, shape independent object tracking based on 2D lidar data

Abstract

The paper presents a new lidar-based approach to object tracking. To this end, range data are recorded by two vehicle-born lidar scanners and registered in a common coordinate system. In contrary to common approaches, particle filters are employed to track the objects. This ensures no linearization of the underlying non-linear process model and, thus, a decreasing estimation error. For the object association, a new method is proposed that considers the knowledge about the object shape as well. Based on a statistical formulation, this ensures a robust object assignment even in ambiguous traffic scenes.