Model-based Offline Vehicle Tracking in Automotive Applications Using a Precise 3D Model

Object tracking aims at estimating the state of moving objects based on remote measurements. To evaluate online algorithms in automotive systems, ground truth data must be acquired, which is a time-consuming and expensive approach. We propose a novel offline approach to generate ground truth data from existing sensor measurements using CAD models. In our approach, we provide error bounds for the localization of the objects based on the measurement noise of a single laser beam and the sensitivity of the point cloud registration. To estimate accurate kinematic states of the vehicle, we apply an extended Rauch-Tung-Striebel smoother on the stored measurements. In experiments with real sensor data, we demonstrate that the performance of the proposed approach is superior to DGPS within the near range.