Abstract
For modern assistance systems, time of flight (tof) cameras play a key role in the perception of the environmental situation. Due to benefits of this sensor type, especially in combination with video sensors, it outperforms typical sensor systems, like stereo cameras. However, tof depth image segmentation is yet not solved satisfactorily. Common approaches use homogeneous constraints and therefore assume objects to be parallel to the camera plane. This consequently leads to segmentation errors. This paper proposes a fast segmentation algorithm for detecting distinct planes using statistical knowledge. By projecting the depth image data along the image axis, u-v-histogram images can be constructed. It is shown, that in the histogram images, 3D planes correspond to line segments. A fast approach for line segment extraction in the histogram images is used to find the relevant planes. The algorithm was successfully tested with real data under varying conditions and can be applied for different tof camera sensors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Thrun, S.: Learning occupancy grid maps with forward sensor models. Autonomous Robots 15, 111–127 (2003)
Skutek, M., Linzmeier, D., Appenrodt, N., Wanielik, G.: A precrash system based on sensor data fusion of laser scanner and short range radars. In: 8th International Conference on Information Fusion 2005, vol. 2 (July 2005)
Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D., Bowyer, K., Eggert, D., Fitzgibbon, A., Fisher, R.: An experimental comparison of range image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 18, 673–689 (1996)
Moosmann, F., Pink, O., Stiller, C.: Segmentation of 3d lidar data in non-flat urban environments using a local convexity criterion. In: 2009 IEEE Intelligent Vehicles Symposium, pp. 215–220 (2009)
Keller, M., Kolb, A.: Real-time simulation of time-of-ight sensors. Simulation Modelling Practice and Theory 17, 967–978 (2009)
Fardi, B., Dousa, J., Gerd, W., Elias, B., Barke, A.: Obstacle detection and pedestrian recognition using a 3D PMD camera. In: Proceedings of Intelligent Vehicles Symposium, pp. 225–230. IEEE, Los Alamitos (2006)
Parvizi, E., Wu, Q.: Real-time approach for adaptive object segmentation in time-of-flight sensors. In: 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2008, vol. 1, pp. 236–240 (November 2008)
Schamm, T., Zöllner, J.M., Vacek, S., Schröder, J., Dillmann, R.: Obstacle detection with a photonic mixing device-camera in autonomous vehicles. International Journal of Intelligent Systems Technologies and Applications 5, 315–324 (2008)
Labayrade, R., Aubert, D., Tarel, J.-P.: Real time obstacle detection in stereovision on non at road geometry through “v-disparity” representation. In: Intelligent Vehicle Symposium, vol. 2, pp. 646–651. IEEE, Los Alamitos (2002)
Hu, Z., Lamosa, F., Uchimura, K.: A complete u-v-disparity study for stereovision based 3d driving environment analysis. In: 3-D Digital Imaging and Modeling, pp. 204–211 (June 2005)
Yip, R.: Line patterns hough transform for line segment detection. In: Proceedings of 1994 IEEE Region 10’s Ninth Annual International Conference on Theme: Frontiers of Computer Technology, TENCON 1994, vol. 1, pp. 319–323 (1994)
Teutsch, M.: Fusion von 6D-Stereo- und Radardaten zur Segmentierung und Attributierung von Objekten im Straßenverkehr. Diploma’s thesis, FZI Forschungszentrum Informatik (January 2009)
Graham, R.: An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set. Information Processing Letters 1, 132–133 (1972)
Schamm, T., Strand, M., Gumpp, T., Kohlhaas, R., Zöllner, J., Dillmann, R.: Vision and ToF-based driving assistance for a personal transporter. In: International Conference on Advanced Robotics, ICAR 2009 (June 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schamm, T., Rönnau, A., Zöllner, J.M. (2010). Fast 3D Time of Flight Data Segmentation Using the U-V-Histogram Approach. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds) Intelligent Robotics and Applications. ICIRA 2010. Lecture Notes in Computer Science(), vol 6424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16584-9_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-16584-9_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16583-2
Online ISBN: 978-3-642-16584-9
eBook Packages: Computer ScienceComputer Science (R0)