Improved object tracking from detailed shape estimation using object local grid maps with stereo

This paper presents a novel approach to environment perception providing detailed information for dynamic objects using occupancy grid maps. The shape representation of dynamic objects is derived from dedicated local grid maps. This allows for a precise contour estimation over time in terms of polylines. In addition to that, the local grid map is used to improve the object tracking by formulating measurements for the Kalman filter update overcoming partial occlusion and over-segmentation of raw data. The algorithm is tested on different data sets and initial results are presented and discussed.