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Line segment-based fast 3D plane extraction using nodding 2D laser rangefinder

Published online by Cambridge University Press:  01 May 2014

Su-Yong An ,
Lae-Kyoung Lee  and
Se-Young Oh
Su-Yong An*
Affiliation:
Institute of Industrial Technology, Samsung Heavy Industries, Daejeon, 305-380, Korea
Lae-Kyoung Lee
Affiliation:
Department of Electrical Eng., Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk 790-784, Korea
Se-Young Oh
Affiliation:
Department of Electrical Eng., Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk 790-784, Korea
*
*Corresponding author. E-mail: [email protected]
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Summary

Three-dimensional (3D) data processing has applications in solving complex tasks such as object recognition, environment modeling, and robotic mapping and localization. Because using raw 3D data without preprocessing is very time-consuming, extraction of geometric features that describe the environment concisely is essential. In this sense, a plane can be a suitable geometric feature due to its simplicity of extraction and the abundance in indoor environments. This paper presents an online incremental plane extraction method using line segments for indoor environments. Our data collection system is based on a “nodding” laser scanner, so we exploit the incremental nature of its data acquisition in which physical rotation and 3D data processing are conducted in parallel. Line segments defined by two end points become supporting elements that comprise a plane, so a large proportion of scan points can be ignored once the line segments are extracted from each scan slice. This elimination of points reduces the algorithm complexity and computation time. Experiments with the tens of complete scan data sets which were acquired from a typical indoor environment demonstrated that our method was at least three times faster than the state-of-the-art methods.

Keywords

3D point cloudLaser scannerLine segmentPlane fitting
Type
Articles
Information
Robotica , Volume 33 , Issue 8 , October 2015 , pp. 1751 - 1774
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Taylor, G. and Kleeman, L., “Robust Range Data Segmentation Using Geometric Primitives for Robotic Applications,” Proceedings of the IASTED International Conference on Signal and Image Processing (2003) pp. 467–472.Google Scholar
2. Weingarten, J. W. and Siegwart, R., “3D SLAM using Planar Segments,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2006) pp. 3062–3067.Google Scholar
3. Hedge, G. M. and Ye, C., “Extraction of Planar Features from SwissRanger SR-3000 Range Images by a Clustering Method Using Normalized Cuts,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2009) pp. 4034–4039.Google Scholar
4. Weingarten, J. W., Gruener, G. and Siegwart, R., “A Fast and Robust 3D Feature Extraction Algorithm for Structured Environment Recognition,” In Proceedings of the International Conference on Advanced Robotics (ICAR) (2003).Google Scholar
5. Weingarten, J. W., Gruener, G. and Siegwart, R., “Probabilistic Plane Fitting in 3D and an Application to Robotic Mapping,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2004) pp. 927–932.Google Scholar
6. Surmann, H., Lingemann, K., Nuchter, A. and Hertzberg, J., “A 3D Laser Range Finder for Autonomous Mobile Robots,” Proceedings of the 32nd International Symposia. on Robotics (ISR) (2001) pp. 153–158.Google Scholar
7. Wulf, O. and Wagner, B., “Fast 3D Scanning Methods for Laser Measurement Systems,” Proceedings of the International Conference on Control Systems and Computer Science (2003) pp. 312–317.Google Scholar
8. Roennau, A., Liebel, G., Schamm, T., Kersher, T. and Dillmann, R., “Robust 3D Scan Segmentation for Teleoperation Tasks in Areas Contaminated by Radiation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 2419–2424.Google Scholar
9. Rusu, R. B., Blodow, N., Marton, Z. C. and Beetz, M., “Close-Range Scene Segmentation and Reconstruction of 3D Point Cloud Maps for Mobile Manipulation in Human Environments,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2009) pp. 1–6.Google Scholar
10. Viejo, D. and Cazorla, M., “3D Plane-Based Egomotion for SLAM on Semi-Structured Environment,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2007) pp. 2761–2766.Google Scholar
11. An, S.-Y., Lee, L.-K. and Oh, S.-Y., “Fast Incremental 3D Plane Extraction from a Collection of 2D Line Segments for 3D Mapping,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012) pp. 4530–4537.Google Scholar
12. Rusu, R. B., Sucan, I. A., Gerkey, B., Chitta, S., Beetz, M. and Kavraki, L. E., “Real-Time Perception-Guided Motion Planning for a Personal Robot,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2009) pp. 4245–4252.Google Scholar
13. Ye, C. and Borenstein, J., “Characterization of a 2D Laser Scanner for Mobile Robot Obstacle Negotiation,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2002) pp. 2512–2518.Google Scholar
14. Rusu, R. B. and Cousins, S., “3D is Here: Point Cloud Library (PCL),” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2011) pp. 1–4.Google Scholar
15. Torr, P. H. S. and Zisserman, A., “MLESAC: A new robust estimator with application to estimating image geometry,” Comput. Vis. Image Underst. 78 (1), 138–156 (2000).Google Scholar
16. Martin, A. F. and Robert, C. B., “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24 (6), 381–395 (1981).Google Scholar
17. Nuchter, A. and Hertzberg, J., “Towards semantic maps for mobile robots,” Robot. Auton. Syst. 56 (11), 915–926 (2008).Google Scholar
18. Deschaud, J.-E., “A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing,” Presented at the International Symposia on 3D Data Processing, Visualization, and Transmission (3DPVT) (2010).Google Scholar
19. Yang, M. Y. and Forstner, W., Plane Detection in Point Cloud Data, Technical Report, TR-IGG-P-2010-01 (Department of Photogrammetry, Institute of Geodesy and Geoinformation, University of Bonn, 2010).Google Scholar
20. Tseng, Y.-H. and Wang, M., “Automatic Plane Extraction from LIDAR Data Based on Octree Splitting and Merging Segmentation,” Proceedings of the IEEE International Geoscience and Remote Sensing Symposia (2005) pp. 3281–3284.Google Scholar
21. Hahnel, D., Burgard, W. and Thrun, S., “Learning Compact 3D Models of Indoor Environments with a Mobile Robot,” Proceedings of the Fourth European Workshop on Advanced Mobile Robots (EUROBOT) (2001) pp. 91–98.Google Scholar
22. Liu, Y., Emery, R., Chakrabarti, D., Burgard, W. and Thrun, S., “Using EM to Learn 3D Models of Indoor Environments with Mobile Robots,” Proceedings of the IEEE International Conference on Machine Learning (ICML) (2001) pp. 329–336.Google Scholar
23. Borrmann, D., Elseberg, J., Lingemann, K. and Nuchter, A., “The 3D Hough transform for plane detection in point cloud: A review and a new accumulator design,” 3D Res. Express 2 (2), 1–13 (2011).Google Scholar
24. Poppinga, J., Vaskevicius, N., Birk, A. and Pathak, K., “Fast Plane Detection and Polygonalization in Noisy 3D Range Images,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2008) pp. 3378–3383.Google Scholar
25. Georgiev, K., Creed, R. T. and Lakaemper, R., “Fast Plane Extraction in 3D Range Data Based on Line Segments,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2011) pp. 3808–3815.Google Scholar
26. Robotis company, [Online] Available http://www.robotis.com/eng.Google Scholar
27. Desai, A. and Huber, D., “Objective Evaluation of Scanning Ladar Configurations for Mobile Robots,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2009) pp. 2182–2189.Google Scholar
28. Nguyen, V., Gachter, S., Martinelli, A., Tomatis, N. and Siegwart, R., “A comparison of line extraction algorithms using 2D range data for indoor mobile robotics,” Auton. Robot. 23 (2), 97–111 (2007).Google Scholar
29. Borges, G. A. and Aldon, M.-J., “Line extraction in 2D range images for mobile robotics,” J. Intell. Robot. Syst 40 (3), 267–297 (2004).Google Scholar
30. Duda, R. O. and Hart, P. E., Pattern Classification and Scene Analysis (John Wiley & Sons, New York, 1973).Google Scholar
31. An, S.-Y., Kang, J.-G., Lee, L.-K. and Oh, S.-Y, “SLAM with Salient Line Feature Extraction in Indoor Environments,” Proceedings of the 11th International Conference on Control, Automation, Robotics, and Vision (2010) pp. 410–416.Google Scholar