Abstract
Detection and tracking of moving objects is an essential problem in situational awareness context and hence crucial for many robotic applications. Here we propose a method for the detection of moving objects with a 3D laser range sensor and a variation of the method for tracking multiple detected objects. The detection procedure starts with the ground extraction using random sample consensus approach for model parameter estimation. The resulting point cloud is then downsampled using voxel grid approach and filtered using a radius outlier rejection method. Within the approach, we have utilized a procedure for building short-term maps of the environment by using the octree data structure. This data structure enables an efficient comparison of the current scan and the short-term local map, thus detecting dynamic parts of scene. The ego-motion of the mobile platform is compensated using the available odometry information, which is rather imperfect, and hence is refined using the iterative closest point registration technique. Furthermore, due to sensor characteristics, the iterative closest point is carried out in 2D between the short-term map and the current, where the non-ground filtered scans are projected onto 2D. The tracking task is based on the joint probabilistic data association filter and Kalman filtering with variable process and measurement noise which take into account velocity and position of the tracked objects. Since this data association approach assumes a constant and known number of objects, we have utilized a specific entropy based track management. The experiments performed using Velodyne HDL-32E laser sensor mounted on top of a mobile platform demonstrate the suitability and efficiency of the proposed method.
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Notes
- 1.
The JPDA filter assumes a constant and known number of objects and we utilize the same formulae thus making it agnostic of the track management, which is separately handled utilizing an entropy based approach described later in the paper.
References
Wang, C.C.: Simultaneous localization, mapping and moving object tracking. Ph.D. thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh (2004)
Darms, M., Rybski, P., Urmson, C.: Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments. In: Intelligent Vehicles Symposium, pp. 1197–1202. IEEE (2008)
Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H.: Junior: the Stanford entry in the urban challenge. J. Field Robot. 25(9), 569–597 (2008)
Navarro-Serment, L.E., Mertz, C., Hebert, M.: Pedestrian detection and tracking using three-dimensional ladar data. Int. J. Rob. Res. 29(12), 1516–1528 (2010)
Petrovskaya, A., Thrun, S.: Model based vehicle detection and tracking for autonomous urban driving. Auton. Rob. 26(2–3), 123–139 (2009)
Kaestner, R., Engelhard, N., Triebel, R., Siegwart, R.: A bayesian approach to learning 3d representations of dynamic environments. In: Proceedings of 12th International Symposium on Experimental Robotics (ISER). Springer Press, Berlin (2010)
Kaestner, R., Maye, J., Siegwart, R.: Generative object detection and tracking in 3d range data. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2012)
Shackleton, J., VanVoorst, B., Hesch, J.: Tracking people with a 360-degree lidar. In: Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 420–426 (2010)
Azim, A., Aycard, O.: Detection, classification and tracking of moving objects in a 3d environment. In: Intelligent Vehicles Symposium, pp. 802–807. IEEE (2012)
Moosmann, F., Fraichard, T.: Motion estimation from range images in dynamic outdoor scenes. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 142–147 (2010)
Moosmann, F., Pink, O., Stiller, C.: Segmentation of 3d lidar data in non-flat urban environments using a local convexity criterion. In: Intelligent Vehicles Symposium, 2009 IEEE, pp. 215–220 (2009)
Steinhauser, D., Ruepp, O., Burschka, D.: Motion segmentation and scene classification from 3d lidar data. In: Intelligent Vehicles Symposium, IEEE, pp. 398–403 (2008)
Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Proc. 50(2), 174–188 (2002)
Miller, I., Campbell, M., Huttenlocher, D.: Efficient unbiased tracking of multiple dynamic obstacles under large viewpoint changes. IEEE Trans. Rob. 27(1), 29–46 (2011)
Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24(6), 843–854 (1979)
Bar-Shalom, Y.: Extension of the probabilistic data association filter to multitarget environment. In: Proceeding of the Fifth Symposium on Nonlinear Estimation (1974)
Vo, B.N., Ma, W.K.: The Gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Proc. 54(11), 4091–4104 (2006)
Mertz, C., Navarro-Serment, L.E.: MacLachlan: moving object detection with laser scanners. J. Field Robot. 30(1), 17–43 (2013)
Morton, P., Douillard, B., Underwood, J.: An evaluation of dynamic object tracking with 3d lidar. In: Australasian Conference on Robotics and Automation (ACRA) (2011)
Cox, I.J.: A review of statistical data association techniques for motion correspondence. Int. J. Comput. Vision 10, 53–66 (1993)
Jurić-Kavelj, S., akulović, M., Petrović, I.: 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), pp. 93–98 (2008)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications 24(6), 381–395 (1981)
Meagher, D.: Geometric modeling using octree encoding. Comput. Graph. Image Process. 19(2), 129–147 (1982)
Wilhelms, J., Gelder, A.V.: Octrees for faster isosurface generation. IEEE Trans. Med. Imaging 19, 739–758 (2000)
Moravec, H., Elfes, A.: High-resolution maps from wide-angle sonar. In: IEEE International Conference on Robotics and Automation (ICRA) (1985)
Yguel, M., Aycard, O., Laugier, C.: Update policy of dense maps: efficient algorithms and sparse representation. In: International Conference Field and Service Robotics, vol. 42, pp. 23–33. Springer, New York (2008)
Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. The MIT Press, Cambridge (2006)
Fortmann, T., Bar-Shalom, Y., Scheffe, M.: Sonar tracking of multiple targets using joint probabilistic data association filter. IEEE J. Oceanic Eng. 8(3), 173–184 (1983)
Bailey, T., Upcroft, B., Durrant-Whyte, H.: Validation gating for non-linear validation gating for non-linear non-Gaussian target tracking. In: International Conference on Information Fusion, pp. 1–6 (2006)
Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House Radar Library, Artech House (1999)
Schulz, D., Burgard, W., Fox, D., Cremers, A.B.: People tracking with mobile robots using sample-based joint probabilistic data association filters. Int. J. Rob. Res. 22(2), 99–116 (2003)
Jurić-Kavelj, S., Marković, I., Petrović, I.: People tracking with heterogeneous sensors using jpdaf with entropy based track management. In: Proceedings of the 5th European Conference on Mobile Robots (ECMR), pp. 31–36 (2011)
Rényi, A.: Probability Theory. Dover books on mathematics, Dover Publications, Incorporated (2007)
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: IEEE International Conference on Robotics and Automation (ICRA), Workshop on Open Source (2009)
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34, 189–206 (2013)
Wurm, K.M., Hornung, A., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems. In: Proceedings of the International Conference on Robotics and Automation (ICRA) (2010)
Rusu, R.B.: The point cloud library (PCL). http://www.pointclouds.org (2014)
Acknowledgments
This work has been supported by European Communitys Seventh Framework Programme under grant agreement no. 285939 (ACROSS) and research project VISTA (EuropeAid/131920/ M/ACT/HR).
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Ćesić, J., Marković, I., Jurić-Kavelj, S., Petrović, I. (2016). Short-Term Map Based Detection and Tracking of Moving Objects with 3D Laser on a Vehicle. In: Filipe, J., Gusikhin, O., Madani, K., Sasiadek, J. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-26453-0_12
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