Automatic Obstacle Classification using Laser and Camera Fusion

Aurelio Ponz, C. H. Rodríguez-Garavito, Fernando García, Philip Lenz, Christoph Stiller, J. M. Armingol

Abstract

State of the art Driving Assistance Systems and Autonomous Driving applications are employing sensor fusion in order to achieve trustable obstacle detection and classification under any meteorological and illumination condition. Fusion between laser and camera is widely used in ADAS applications in order to overcome the difficulties and limitations inherent to each of the sensors. In the system presented, some novel techniques for automatic and unattended data alignment are used and laser point clouds are exploited using Artificial Intelligence techniques to improve the reliability of the obstacle classification. New approaches to the problem of clustering sparse point clouds have been adopted, maximizing the information obtained from low resolution lasers. After improving cluster detection, AI techniques have been used to classify the obstacle not only with vision, but also with laser information. The fusion of the information acquired from both sensors, adding the classification capabilities of the laser, improves the reliability of the system.

References

  1. Debattisti, S, Mazzei, L & Panciroli, M 2013. Automated extrinsic laser and camera inter-calibration using triangular targets. Intelligent Vehicles Symposium (IV), 2013 IEEE, 2013, pp. 696-701.
  2. Cortes, C & Vapnik, V 1995, Support vector network, Machine Learning, vol. 20, pp. 1-25.
  3. Fremont, V & Bonnifait, P 2008. Extrinsic calibration between a multi-layer lidar and a camera. 2008 IEEE Int. Conf. Multisens. Fusion Integr. Intell. Syst., 2008.
  4. García, F, Jiménez, F, Naranjo, JE, Zato, JG, Aparicio, F, Armingol, JM & de la Escalera, A. 2012. Environment perception based on LIDAR sensors for real road applications.
  5. García, F, García, J, Ponz, A, de la Escalera, A & Armingol, JM 2014. Context Aided Pedestrian Detection for Danger Estimation Based on Laser and Computer Vision. Expert Systems with Applications, Vol: 41 (15), pp.6646-6661.
  6. Kaempchen, N, Buehler, M & Dietmayer, K 2005. Feature-level fusion for free-form object tracking using laserscanner and video. IEEE Proceedings Intelligent Vehicles Symposium 2005, pp. 453-458, 2005.
  7. Kwak, K, Huber, DF, Badino, H & Kanade, T. 2011 .Extrinsic calibration of a single line scanning lidar and a camera. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 3283-3289, 2011.
  8. Li, Y, Ruichek, Y & Cappelle, D 2011. 3D triangulation based extrinsic calibration between a stereo vision system and a LIDAR. 14th Int. IEEE Conf. Intell. Transp. Syst., pp. 797-802, 2011.
  9. Li, Y, Liu, Y, Dong, L, Cai, X 2007. An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features, IEEE/RSJ Int. Conf. Intell. Robot. Syst.
  10. Lisca, G, Jeong, PJP & Nedevschi, S 2010. Automatic one step extrinsic calibration of a multi layer laser relative to a stereo camera. Intell. Comput. Commun. Process. (ICCP), 2010 IEEE Int. Conf., 2010.
  11. Martín, D, García, F, Musleh, B, Olmeda, D, Marín, P, Ponz, A, Rodríguez, CH, Al-Kaff, A, de la Escalera, A & Armingol, JM 2014. IVVI 2.0: An intelligent vehicle based on computational perception. Expert Systems with Applications 41.
  12. Premebida, C, Ludwig, O & Nunes, U 2009. LIDAR and Vision-Based Pedestrian Detection System. Journal of Field Robotics, vol. 26, no. Iv, pp. 696-711, 2009.
  13. Premebida, C, Ludwig, O, Silva, M & Nunes, U 2010. A Cascade Classifier applied in Pedestrian Detection using Laser and Image-based Features. Transportation, pp. 1153-1159, 2010.
  14. Rodríguez-Garavito, CH, Ponz, A, García, F, Martín, D, de la Escalera, A & Armingol, JM 2014. Automatic Laser and Camera Extrinsic Calibration for Data Fusion Using Road Plane.
  15. Spinello, L & Siegwart, R, 2008. Human detection using multimodal and multidimensional features. IEEE International Conference on Robotics and Automation, 3264-3269. DOI: 10.1109/ROBOT.2009.4543708.
  16. WHO, 2009. Global status report on road safety. Time for action. WHO library cataloguing-in-publication data, World Health Organization 2009, ISBN 978-9- 241563-84-0, Geneva, Switzerland.
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Paper Citation


in Harvard Style

Ponz A., H. Rodríguez-Garavito C., García F., Lenz P., Stiller C. and M. Armingol J. (2015). Automatic Obstacle Classification using Laser and Camera Fusion . In Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-109-0, pages 19-24. DOI: 10.5220/0005459600190024


in Bibtex Style

@conference{vehits15,
author={Aurelio Ponz and C. H. Rodríguez-Garavito and Fernando García and Philip Lenz and Christoph Stiller and J. M. Armingol},
title={Automatic Obstacle Classification using Laser and Camera Fusion},
booktitle={Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2015},
pages={19-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005459600190024},
isbn={978-989-758-109-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Automatic Obstacle Classification using Laser and Camera Fusion
SN - 978-989-758-109-0
AU - Ponz A.
AU - H. Rodríguez-Garavito C.
AU - García F.
AU - Lenz P.
AU - Stiller C.
AU - M. Armingol J.
PY - 2015
SP - 19
EP - 24
DO - 10.5220/0005459600190024