3D Non-Contact Building Survey Technique

Abstract:

This paper presents a non-contact 3D environments mapping technique using mobile robots with different perception devices such as stereo camera, structural light camera or custom made 3D laser scanner. The custom developed non-contact scanning device is suitable for making building surveillance both in small and large scales. These measurements can be further used for urban region planning as well as for navigation purposes for autonomous agents such as mobile vehicles.. The large maps can be built from successive scan merging in an iterative manner. For such an approach is essential the initial matching between two measurements, which in our proposed method was performed using special extracted features from the measured data sets. The registered maps can be further used for perceptual purposes, including object segmentation or planning maps for the mobile agents. For both types of applications, there are given experimental results obtained from the proposed registration and segmentation algorithms.

• Periodical: Advanced Engineering Forum (Volumes 8-9)
• Pages: 584-592
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.8-9.584
• Online since: June 2013
• Authors: Levente Tamas, Gheorghe Lazea
• Keywords: 3D Laser Scanner, Pointcloud Processing, Scan Registration, Segmentation

References

[1] F. Maurelli, D. Droeschel, T. Wisspeintner and H. M. S., A 3D Laser Scanner System for Autonomous Vehicle Navigation,, Proceedings of the International Conference on Advanced Robotics (ICAR), pp.1-6, (2009).

[2] J. Surmann, A 3D Laser Range Finder for Autonomous Mobile Robots, in Proceedings of the International Symposium on Robotics, Zurich, (2001).

[3] J. W. Weingarten, G. Gruener and R. Siegwart, A State-of-the-Art 3D Sensor for Robot Navigation,, IEEE/RSJ International Conference on Intelligent Robots andSystems (IROS) 2004, pp.2155-2160.

DOI: 10.1109/iros.2004.1389728

[4] O. &. W. B. Wulf, Fast 3D-Scanning Methods for Laser Measurement Systems, in International Conference on Control Systems and Computer Science, Bucharest, (2005).

[5] D. Chetverikov and D. a. P. K. Stepanov, Robust euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm, Image and Vision Computing, (2005).

DOI: 10.1016/j.imavis.2004.05.007

[6] M. Magnusson, T. Duckett and A. J. Lilienthal, Scan Registration for Autonomous Mining Vehicles Using 3D-NDT, Journal of Field Robotics, vol. 24, pp.803-827, (2007).

DOI: 10.1002/rob.20204

[7] A. Howard, D. F. Wolf and G. S. Sukhatme, Towards 3D Mapping in Large Urban Environments,, International Conference on Intelligent Robots andSystems (IROS 2004, pp.419-426.

DOI: 10.1109/iros.2004.1389388

[8] A. Zhang, S. Hu, Y. Chen, H. Liu, F. Yang and J. Liu, Fast Continuous 360 Degree Color 3D Laser Scanner, International Archives of the Photogrammetry, Remote Sensingand Spatial Information Sciences vol. 36, pp.409-415, (2008).

[9] B. Morisset, R. B. Rusu, A. Sundaresan, K. Hauser, M. Agrawal, J. -C. Latombe and M. Beetz, Leaving Flatland: Toward Real-Time 3D Navigation,, IEEE International Conference on Robotics and Automation (ICRA), pp.3786-3792, (2009).

DOI: 10.1109/robot.2009.5152715

[10] K. Ohno, S. Tadokoro, K. Nagatani, E. Koyanagi and T. Yoshida, Trials of 3-D Map Construction Using the Tele-operated Tracked Vehicle Kenaf at Disaster City,, nternational conference on Intelligent robots and systems(IROS) , (2010).

DOI: 10.1109/robot.2010.5509722

[11] R. Kaushik, J. Xiao, W. Morris and Z. Zhu, 3D laser scan registration of dual-robot system using vision,, nternational conference on Intelligent robots and systems (IROS), (2009).

DOI: 10.1109/iros.2009.5354773

[12] A. Birk, N. Vaskevicius, K. Pathak, S. Schwertfeger, J. Poppinga and H. Buelow, 3D Perception and Modeling: Motion Level Teleoperation and Intelligent Autonomous Functions, IEEE Robotics and Automation Magazine (RAM), vol. 6, pp.53-60, (2009).

DOI: 10.1109/mra.2009.934822

[13] R. B. Rusu and S. Cousins, 3D is here: Point Cloud Library (PCL),, International Conference on Robotics and Automation (ICRA) pp.1-4, (2011).

DOI: 10.1109/icra.2011.5980567

[14] P. Henry, M. Krainin, E. Herbst, X. Ren and D. Fox, RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments,, Advanced Reasoning with Depth Cameras Workshop in conjunctionwith the RSS conference, (2010).

DOI: 10.1007/978-3-642-28572-1_33

[15] N. Vandapel, D. F. Huber, A. Kapuria and M. Hebert, Natural Terrain Classification Using 3-D Ladar Data, IEEE International Conference on Robotics and Automation (ICRA) pp.5117-522, (2004).

DOI: 10.1109/robot.2004.1302529

[16] Henriksen, L. & Krotkov, E. Natural Terrain Hazard Detection with a Laser Rangefinder Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 1997, 968-973.

DOI: 10.1109/robot.1997.614260

[17] J. J. Leger and P. C. Biesiadecki, Tradeoffs Between Directed and Autonomous Driving on the Mars Exploration Rovers, The International Journal of Robotics Research, vol. 26, pp.91-104, (2007).

DOI: 10.1177/0278364907073777

[18] H. Surmann, K. Lingemann, A. Nuchter and J. Hertzberg, A 3D Laser Range Finder for Autonomous Mobile Robots,, Robotics and Autonomous Systems, pp.181-192, (2001).

DOI: 10.1016/j.robot.2003.09.004

[19] A. Nuechter, 3D Robotic Mapping, Springer, p.210, (2009).

[20] Z. Zhang, Iterative Point Matching for Registration of Free-Form Curves, Technical Report No. RR-1658, (1992).

[21] L. Tamas and L. C. Goron, 3D Map Building with Mobile Robots, in IEEE MED Conference, pp.615-621, (2012).

DOI: 10.1109/med.2012.6265627

[22] G. C. Sharp, S. W. Lee and D. K. Wehe, ICP Registration Using Invariant Features, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, pp.90-102, (2002).

DOI: 10.1109/34.982886

[23] B. Steder, R. B. Rusu and K. Konolige, Point Feature Extraction on 3D Range Scans Taking into Account Object Boundaries, IROS IEEE Conference, (2011).

DOI: 10.1109/icra.2011.5980187