Long Range Optical Truck Tracking

Christian Winkens, Dietrich Paulus

Abstract

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 70meters 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.

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Paper Citation


in Harvard Style

Winkens C. and Paulus D. (2017). Long Range Optical Truck Tracking . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 330-339. DOI: 10.5220/0006296003300339


in Bibtex Style

@conference{icaart17,
author={Christian Winkens and Dietrich Paulus},
title={Long Range Optical Truck Tracking},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={330-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006296003300339},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Long Range Optical Truck Tracking
SN - 978-989-758-220-2
AU - Winkens C.
AU - Paulus D.
PY - 2017
SP - 330
EP - 339
DO - 10.5220/0006296003300339