Long Range Optical Truck Tracking

Platooning applications require precise knowledge about position and orientation (pose) of the leading vehicle especially in rough terrain. We present an optical solution for a robust pose estimation using artificial markers and a camera as the only sensor. Temporal coherence of image sequences is used in a Kalman filter to obtain precise estimates. Furthermore based on the marker detections we utilize an adaptive model building algorithm which learns a keypoint based representation of the leading vehicle at runtime. The model is continuously updated and allows a markerless tracking of the vehicle for up to 70 meters even when driving at high velocities. The system is designed for and tested in off-road scenarios. A pose evaluation is performed in a simulation testbed.