Detection and Tracking of Dynamic Objects using 3D Laser Range Sensor on a Mobile Platform

Josip Ćesić, Ivan Marković, Srećko Jurić-Kavelj, Ivan Petrović


In this paper we present an algorithm for detection, extraction and tracking of moving objects using a 3D laser range sensor. First, ground extraction is performed using random sample consensus for model parameter estimation. Afterwards, to downsample the point cloud, a voxel grid filtering is executed and octree data structure is used. This data structure enables an efficient detection of differences between two consecutive point clouds, based on which clustering of dynamic parts of the cloud is performed. The obtained clusters are then expanded over the set of static voxels in order to cover entire objects. In order to account for ego-motion an iterative closest point registration technique with an initial transformation guess obtained by odometry of the platform is used. As the final step, we present a tracking algorithm based on joint probabilistic data association (JPDA) filter with variable process and measurement noise taking into account velocity and position of the tracked objects. However, JPDA filter assumes a constant and known number of objects in the scene, and therefore we use track management based on entropy. Experiments are performed using a setup consisting of a Velodyne HDL-32E mounted on top of a mobile platform in order to verify the developed algorithms.


  1. Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Signal Processing, IEEE Transactions on, 50(2):174-188.
  2. Azim, A. and Aycard, O. (2012). Detection, classification and tracking of moving objects in a 3d environment. In Intelligent Vehicles Symposium, pages 802- 807. IEEE.
  3. Bar-Shalom, Y. (1974). Extension of the probabilistic data association filter to multitarget environment. Proc. Fifth Symp. on Nonlinear Estimation.
  4. Besl, P. J. and McKay, N. D. (1992). A Method for Registration of 3-D Shapes. IEEE Trans. Pattern Anal. Mach. Intell., 14(2):239-256.
  5. Blackman, S. and Popoli, R. (1999). Design and Analysis of Modern Tracking Systems. Artech House Radar Library. Artech House.
  6. Cox, I. J. (1993). A review of statistical data association techniques for motion correspondence. International Journal of Computer Vision, 10:53-66.
  7. Darms, M., Rybski, P., and Urmson, C. (2008). Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments. In Intelligent Vehicles Symposium, 2008 IEEE, pages 1197-1202.
  8. Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381-395.
  9. Juric-Kavelj, S., Ðakulovic, M., and Petrovic, I. (2008). Tracking multiple moving objects using adaptive sample-based joint probabilistic data association filter. In Proceedings of 5th International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2008), pages 93-98.
  10. Juric-Kavelj, S., Markovic, I., and Petrovic, I. (2011). People tracking with heterogeneous sensors using jpdaf with entropy based track management. In Proceedings of the 5th European Conference on Mobile Robots (ECMR2011), pages 31-36.
  11. Kaestner, R., Engelhard, N., Triebel, R., and Siegwart, R. (2010). A bayesian approach to learning 3d representations of dynamic environments. In Proceedings of The 12th International Symposium on Experimental Robotics (ISER), Berlin. Springer Press.
  12. Kaestner, R., Maye, J., and Siegwart, R. (2012). Generative object detection and tracking in 3d range data. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA).
  13. Meagher, D. (1982). Geometric modeling using octree encoding. Computer Graphics and Image Processing, 19(2):129-147.
  14. Mertz, C., Navarro-Serment, L. E., and MacLachlan (2013). Moving object detection with laser scanners. Journal of Field Robotics, 30(1):17-43.
  15. Miller, I., Campbell, M., and Huttenlocher, D. (2011). Efficient unbiased tracking of multiple dynamic obstacles under large viewpoint changes. Trans. Rob., 27(1):29- 46.
  16. Montemerlo, M., Becker, J., Bhat, S., and Dahlkamp, H. (2008). Junior: The stanford entry in the urban challenge. J. Field Robot., 25(9):569-597.
  17. Moosmann, F. and Fraichard, T. (2010). Motion estimation from range images in dynamic outdoor scenes. In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages 142-147.
  18. Moosmann, F., Pink, O., and Stiller, C. (2009). Segmentation of 3d lidar data in non-flat urban environments using a local convexity criterion. In Intelligent Vehicles Symposium, 2009 IEEE, pages 215-220.
  19. Morton, P., Douillard, B., and Underwood, J. (2011). An evaluation of dynamic object tracking with 3d lidar. In 2011 Australasian Conference on Robotics and Automation (ACRA). ACRA.
  20. Navarro-Serment, L. E., Mertz, C., and Hebert, M. (2010). Pedestrian detection and tracking using threedimensional ladar data. The International Journal of Robotics Research, Special Issue on the Seventh International Conference on Field and Service Robots, 29(12):1516 - 1528.
  21. Petrovskaya, A. and Thrun, S. (2009). Model based vehicle detection and tracking for autonomous urban driving. Auton. Robots, 26(2-3):123-139.
  22. Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., and Ng, A. (2009). ROS: an open-source Robot Operating System. IEEE Int. Conf. on Robotics and Automation (ICRA), Workshop on Open Source.
  23. Reid, D. (1979). An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, 24(6):843- 854.
  24. Rényi, A. (2007). Probability Theory. Dover books on mathematics. Dover Publications, Incorporated.
  25. Rusu, R. B. (2014). The Point Cloud Library (PCL).
  26. Schulz, D., Burgard, W., Fox, D., and Cremers, A. B. (2003). People tracking with mobile robots using sample-based joint probabilistic data association filters. The International Journal of Robotics Research, 22(2):99-116.
  27. Shackleton, J., VanVoorst, B., and Hesch, J. (2010). Tracking people with a 360-degree lidar. In Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 7810, pages 420-426, Washington, DC, USA. IEEE Computer Society.
  28. Steinhauser, D., Ruepp, O., and Burschka, D. (2008). Motion segmentation and scene classification from 3d lidar data. In Intelligent Vehicles Symposium, 2008 IEEE, pages 398-403.
  29. Vo, B.-N. and Ma, W.-K. (2006). The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 54(11):4091-4104.
  30. Wang, C.-C. (2004). Simultaneous Localization, Mapping and Moving Object Tracking. PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
  31. Wilhelms, J. and Gelder, A. V. (2000). Octrees for faster isosurface generation. IEEE Transactions on Medical Imaging, 19:739-758.

Paper Citation

in Harvard Style

Ćesić J., Marković I., Jurić-Kavelj S. and Petrović I. (2014). Detection and Tracking of Dynamic Objects using 3D Laser Range Sensor on a Mobile Platform . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 110-119. DOI: 10.5220/0005057601100119

in Bibtex Style

author={Josip Ćesić and Ivan Marković and Srećko Jurić-Kavelj and Ivan Petrović},
title={Detection and Tracking of Dynamic Objects using 3D Laser Range Sensor on a Mobile Platform},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},

in EndNote Style

JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Detection and Tracking of Dynamic Objects using 3D Laser Range Sensor on a Mobile Platform
SN - 978-989-758-040-6
AU - Ćesić J.
AU - Marković I.
AU - Jurić-Kavelj S.
AU - Petrović I.
PY - 2014
SP - 110
EP - 119
DO - 10.5220/0005057601100119