Abstract
This chapter proposes a sequential hybrid method for 3D Lidar data segmentation. The presented approach provides more reliable results against the under-segmentation issue, i.e., assigning several objects to one segment, by combining spatial and temporal information to discriminate nearby objects in the data. For instance, it is common for pedestrians to get under-segmented with their neighboring objects. Combining temporal and spatial cues allow us to resolve such ambiguities. After getting the temporal features, we propose a sequential hybrid approach using the mean-shift method and a sequential variant of distance dependent Chinese Restaurant Process (ddCRP). The segmentation blobs are spatially extracted from the scene with a connected components algorithm. Then, as a post-processing, the mean-shift seeks the number of possible objects in the state space of each blob. If the mean-shift algorithm determines an under-segmentation, the sequential ddCRP performs the final partition in this blob. Otherwise, the queried blob remains the same and it is assigned as a segment. Compared to the other recent methods in the literature, our framework significantly reduces the under-segmentation errors while running in real time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Blei, D.M., Frazier, P.I.: Distance dependent Chinese restaurant processes. J. Mach. Learn. Res. 12, 2461–2488 (2011)
Tuncer, M.A.Ç., Schulz, D.: Monte Carlo based distance dependent Chinese restaurant process for segmentation of 3D LIDAR data using motion and spatial features. In: 18th IEEE International Conference on Information Fusion (Fusion), Washington, DC, pp. 112–118 (2015)
Tuncer, M.A.Ç., Schulz, D.: A hybrid method using temporal and spatial information for 3D LIDAR data segmentation. In: 14th International Conference on Informatics in Control, Automation and Robotics, Madrid, Spain (2017). https://doi.org/10.5220/0006471101620171
Tuncer, M.A.Ç., Schulz, D.: Sequential distance dependent Chinese restaurant processes for motion segmentation of 3D LIDAR data. In: 19th IEEE International Conference on Information Fusion (Fusion), Heidelberg, Germany, pp. 758–765 (2016)
Tuncer, M.A.Ç., Schulz, D.: Integrated object segmentation and tracking for 3D LIDAR data. In: 13th International Conference on Informatics in Control, Automation and Robotics, Lisbon, Portugal, pp. 344–351 (2016)
Douillard, B., et al.: On the segmentation of 3D LIDAR point clouds. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2798–2805 (2011)
Moosmann, F., Pink, O., Stiller, C.: Segmentation of 3D LIDAR data in non-flat urban environments using a local convexity criterion. In: IEEE Intelligent Vehicles Symposium, pp. 215–220 (2009)
Urmson, C., et al.: Autonomous driving in urban environments: Boss and the urban challenge. J. Field Robot. 25, 425–466 (2008) (Wiley Online Library)
Montemerlo, M., et al.: Junior: The Stanford entry in the urban challenge. J. Field Robot. 25, 569–597 (2008) (Wiley Online Library)
Petrovska, A., Thrun, S.: Model Based Vehicle Tracking for Autonomous Driving in Urban Environments. The MIT Press, Zurich (2008)
Morton, P., Douillard, B., Underwood, J.: An evaluation of dynamic object tracking with 3D LIDAR. In: Proceedings of Australasian Conference on Robotics and Automation, Melbourne, Australia (2011)
Choi, J., Ulbrich, S., Lichte, B., Maurer, M.: Multi-target tracking using a 3D-LIDAR sensor for autonomous vehicles. In: Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems. The Hague, The Netherlands (2013)
Azim, A., Aycard, O.: Detection, classification and tracking of moving objects on a 3D environment. In: Intelligent Vehicles Symposium, Alcala de Henares, Spain (2012)
Klasing, K., Wollherr, D., Buss, M.: A clustering method for efficient segmentation of 3D laser data. In: IEEE Robotics and Automation Conference (ICRA), pp. 4043–4048 (2008)
Steinhauser, D., Ruepp, O., Burschka, D.: Motion segmentation and scene classification from 3D LIDAR data. In: IEEE Intelligent Vehicles Symposium, pp. 398–403 (2008)
Asvadi, A., Peixoto, P., Nunes, U.: Detection and tracking of moving objects using 2.5 D motion grids. In: IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), pp. 788–793 (2015)
Teichman, A., Levinson, J., Thrun, S.: Towards 3D object recognition via classification of arbitrary object tracks. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4034–4041 (2011)
Himmelsbach, M., Wuensche, H.J.: Tracking and classification of arbitrary objects with bottom-up top-down detection. In: Intelligent Vehicles Symposium, Alcala de Henares, Spain (2012)
Held, D., Guillory, D., Rebsamen, B., Thrun, S., Savarese, S.: A probabilistic framework for real-time 3D segmentation using spatial, temporal, and semantic cues. In: Proceedings of Robotics: Science and Systems, AnnArbor, Michigan (2016). https://doi.org/10.15607/RSS.2016.XII.024
Herbst, E., Ren, X., Fox, D.: RGB-D flow: dense 3-D motion estimation using color and depth. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 2276–2282 (2013)
Stckler, J., Behnke, S.: Efficient dense rigid-body motion segmentation and estimation in RGB-D video. Int. J. Comput. Vis. 113(3), 233–245 (2015)
Hickson, S., Birchfield, S., Essa, I., Christensen, H.: Efficient hierarchical graph-based segmentation of RGBD videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 344–351 (2014)
Xu, C., Whitt, S., Corso, J.J.: Flattening supervoxel hierarchies by the uniform entropy slice. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2240–2247 (2013)
Xu, C., Xiong, C., Corso, J.J.: Streaming hierarchical video segmentation. In: European Conference on Computer Vision, pp. 626–639. Springer, Berlin (2012)
Driessen, J.N., Biemond, J.: Motion field estimation for complex scenes. In: Proceedings of SPIE, pp. 511–521 (1991)
Sellent, A., Eisemann, M., Goldlucke, B., Cremers, D., Magnor, M.: Motion field estimation from alternate exposure images. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1577–1589 (2011) (IEEE Computer Society)
Stein, F.: Efficient computation of optical flow using the census transform. In: Proceedings of the 26th DAGM Symposium. Lecture Notes in Computer Science, vol. 3175, pp. 79–86 (2004)
Rabe, C., Franke, U., Gehrig, S.: Fast detection of moving objects in complex scenarios. In: Proceedings of the IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, pp. 398–403 (2007)
Rabe, C., Müller, T., Wedel, A., Franke, U.: Dense, robust and accurate 3D motion field estimation from stereo image sequences in real-time. In: Proceedings of European Conference on Computer Vision, Heraklion, Greece (2010)
Franke, U., Rabe, C., Badino, H., Gehrig, S.K.: 6D vision: fusion of stereo and motion for robust environment perception. In: Proceedings of the 27th German Association for Pattern Recognition (DAGM) Symposium, Vienna, Austria, pp. 216–223 (2005)
Li, Q., et al.: Motion field estimation for a dynamic scene using a 3D LIDAR. Sensors 14, 16672–16691 (2014)
Pitman, J.: Combinatorial Stochastic Process. Lecture Notes for St. Flour Summer School. Springer, New York (2002)
Socher, R., Maas, A., Manning, C.D.: Spectral Chinese restaurant process: nonparametric clustering based on similarities (2012)
Yang, C., Xie, L., Zhou, X.: Unsupervised broadcast news story segmentation using distance dependent Chinese restaurant processes. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), pp. 4062–4066 (2014)
Ghosh, S., Ungureanu, A.B., Sudderth, E.B., Blei, D.: Spatial distance dependent Chinese restaurant processes for image segmentation. In: NIPS, pp. 1476–1484 (2011)
Ghosh, S., Sudderth, E., Loper, E., Black, M.: From deformations to parts: motion-based segmentation of 3D objects. In: Advances in Neural Information Processing Systems, pp. 2006–2014 (2012)
Velodyne LIDAR, High Definition LIDAR HDL-64E S2 Specifications. http://velodynelidar.com/lidar/hdlproducts/hdl64e.aspx
Bar-Shalom, Y.: Tracking and Data Association. Academic Press Professional, Inc., Boston (1987)
Alparone, L., Barni, M., Bartolini, F., Cappellini, V.: Adaptively weighted vector-median filters for motion-fields smoothing. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2267–2270 (1996)
Raiffa, H., Schlaifer, R.: Applied Statistical Decision Theory. Graduate School of Business Administration. Harvard University, Division of Research (1961)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 721–741 (1984)
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis. Texts in Statistical Science, 2nd edn. Chapman and Hall/CRC, Boca Raton (2003)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)
Fritsch, J., Kuhnl, T., Geiger, A.: A new performance measure and evaluation benchmark for road detection algorithms. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1693–1700 (2013)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: IEEE Computer Vision and Pattern Recognition Conference (CVPR), pp. 3354–3361 (2012)
Acknowledgements
We acknowledge the support by the EU’s Seventh Framework Programme under grant agreement no. 607400 (TRAX, Training network on tRAcking in compleX sensor systems) http://www.trax.utwente.nl/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tuncer, M.A.Ç., Schulz, D. (2020). 3D Lidar Data Segmentation Using a Sequential Hybrid Method. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics . ICINCO 2017. Lecture Notes in Electrical Engineering, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-030-11292-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-11292-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11291-2
Online ISBN: 978-3-030-11292-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)