Improving Lidar Data Evaluation for Object Detection and Tracking Using a Priori Knowledge and Sensorfusion

David Wittmann, Frederic Chucholowski, Markus Lienkamp

2014

Abstract

This paper presents a new approach to improve lidar data evaluation on the basis of using a priori knowledge. In addition to the common I- and L-shapes, the directional IS-shape, the C-shape for pedestrians and the E-shape for bicycles are introduced. Considering the expected object shape and predicted position enables effective interpretation even of poor measurement values. Therefore a classification routine is utilized to distinguish between three classes (cars, bicycles, pedestrians). The tracking operation with Kalman filters is based on class specific dynamic models. The fusion of radar objects with the used a priori knowledge improves the quality of the lidar evaluation. Experiments with real measurement data showed good results even with a single layer lidar scanner.

References

  1. Fayad, F. and Cherfaoui, V. (2007). Tracking objects using a laser scanner in driving situation based on modeling target shape. In 2007 IEEE Intelligent Vehicles Symposium, pages 44-49.
  2. Fortin, B., Lherbier, R., and Noyer, J.-C. (2012). Feature extraction in scanning laser range data using invariant parameters: Application to vehicle detection. IEEE Transactions on Vehicular Technology, 61(9):3838- 3850.
  3. Fuerstenberg, K. C., Linzmeier, D. T., and Dietmayer, K. C. (2003). Pedestrian recognition and tracking of vehicles using a vehicle based multilayer laserscanner. In Proceedings of ITS 2003, 10th World Congress on Intelligent Transport Systems.
  4. Grewal, M. S. and Andrews, A. P. (2008). Kalman filtering: Theory and practice using MATLAB. John Wiley, Hoboken, 3 edition.
  5. Kaempchen, N., Buehler, M., and Dietmayer, K. (2005). Feature-level fusion for free-form object tracking using laserscanner and video. In IEEE Proceedings. Intelligent Vehicles Symposium, 2005, pages 453-458.
  6. Lindl, R. (2008). Tracking von Verkehrsteilnehmern im Kontext von Multisensorsystemen. PhD thesis, Technische Universität München, München.
  7. Mendes, A., Bento, L., and Nunes, U. (June 14-17, 2004). Multi-target detection and tracking with a laserscanner. In IEEE Intelligent Vehicles Symposium, 2004, pages 796-801.
  8. Sparbert, J., Dietmayer, K., and Streller, D. (25-29 Aug. 2001). Lane detection and street type classification using laser range images. In 2001 IEEE Intelligent Transportation Systems. Proceedings, pages 454-459.
  9. Vu, T.-D., Aycard, O., and Appenrodt, N. (2007). Online localization and mapping with moving object tracking in dynamic outdoor environments. In 2007 IEEE Intelligent Vehicles Symposium, pages 190-195.
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Paper Citation


in Harvard Style

Wittmann D., Chucholowski F. and Lienkamp M. (2014). Improving Lidar Data Evaluation for Object Detection and Tracking Using a Priori Knowledge and Sensorfusion . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 794-801. DOI: 10.5220/0005117707940801


in Bibtex Style

@conference{icinco14,
author={David Wittmann and Frederic Chucholowski and Markus Lienkamp},
title={Improving Lidar Data Evaluation for Object Detection and Tracking Using a Priori Knowledge and Sensorfusion},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={794-801},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005117707940801},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Improving Lidar Data Evaluation for Object Detection and Tracking Using a Priori Knowledge and Sensorfusion
SN - 978-989-758-039-0
AU - Wittmann D.
AU - Chucholowski F.
AU - Lienkamp M.
PY - 2014
SP - 794
EP - 801
DO - 10.5220/0005117707940801