TY - GEN
T1 - Towards autonomous driving in a parking garage
T2 - 2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
AU - Ibisch, Andre
AU - Stumper, Stefan
AU - Altinger, Harald
AU - Neuhausen, Marcel
AU - Tschentscher, Marc
AU - Schlipsing, Marc
AU - Salinen, Jan
AU - Knoll, Alois
PY - 2013
Y1 - 2013
N2 - In this paper, we propose a new approach for localization and tracking of a vehicle in a parking garage, based on environment-embedded LIDAR sensors. In particular, we present an integration of data from multiple sensors, allowing to track vehicles in a common, parking garage coordinate system. In order to perform detection and tracking in realtime, a combination of appropriate methods, namely a grid-based approach, a RANSAC algorithm, and a Kalman filter is proposed and evaluated. The system achieves highly confident and exact vehicle positioning. In the context of a larger framework, our approach was used as a reference system to enable autonomous driving within a parking garage. In our experiments, we showed that the proposed algorithm allows a precise vehicle localization and tracking. Our system's results were compared to human-labeled ground-truth data. Based on this comparison we prove a high accuracy with a mean lateral and longitudinal error of 6.3cm and 8.5 cm, respectively.
AB - In this paper, we propose a new approach for localization and tracking of a vehicle in a parking garage, based on environment-embedded LIDAR sensors. In particular, we present an integration of data from multiple sensors, allowing to track vehicles in a common, parking garage coordinate system. In order to perform detection and tracking in realtime, a combination of appropriate methods, namely a grid-based approach, a RANSAC algorithm, and a Kalman filter is proposed and evaluated. The system achieves highly confident and exact vehicle positioning. In the context of a larger framework, our approach was used as a reference system to enable autonomous driving within a parking garage. In our experiments, we showed that the proposed algorithm allows a precise vehicle localization and tracking. Our system's results were compared to human-labeled ground-truth data. Based on this comparison we prove a high accuracy with a mean lateral and longitudinal error of 6.3cm and 8.5 cm, respectively.
UR - https://www.scopus.com/pages/publications/84892395282
U2 - 10.1109/IVS.2013.6629569
DO - 10.1109/IVS.2013.6629569
M3 - Conference contribution
AN - SCOPUS:84892395282
SN - 9781467327558
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 829
EP - 834
BT - 2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
Y2 - 23 June 2013 through 26 June 2013
ER -