Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-26T06:12:08.178Z Has data issue: false hasContentIssue false

Robust 2D map building with motion-free ICP algorithm for mobile robot navigation

Published online by Cambridge University Press:  15 August 2016

YoSeop Hwang
Affiliation:
Department of Electronic Engineering, Pusan National University, Busan 609-735, Korea E-mail: mmx001@pusan.ac.kr, jmlee@pusan.ac.kr
JangMyung Lee*
Affiliation:
Department of Electronic Engineering, Pusan National University, Busan 609-735, Korea E-mail: mmx001@pusan.ac.kr, jmlee@pusan.ac.kr
*
*Corresponding author. E-mail: jmlee@pusan.ac.kr

Summary

A new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm.

Type
Articles
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Allee, G., “OctoMap: An efficient probabilistic 3D mapping framework based on octrees,” Auton. Robots 34 (3), 189206 (2013).Google Scholar
2. Kummerle, R., Ruhnke, M., Steder, B., Stachniss, C. and Burgard, W., “A Navigation System for Robots Operating in Crowded Urban Environments,” Proceedings of IEEE Conference on Robotics and Automation ICRA, (May 2013) pp. 3225–3232.CrossRefGoogle Scholar
3. Henry, P., Krainin, M., Herbst, E., Ren, X. and Fox, D., “RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments,” Exp. Robot., Springer Berlin Heidelberg, 477491 (2014).CrossRefGoogle Scholar
4. Pacheco, J., Ascencio, J. and Mancha, J., “Visual simultaneous localization and mapping: A survey,” Artif. Intell. Rev. 43 (1), 5581 (2015).Google Scholar
5. Mirkhania, M., Forsatib, R., Shahric, A. and Moayedikiad, A., “A novel efficient algorithm for mobile robot localization,” Robot. Auton. Syst. 61 (9), 920931 (2013).CrossRefGoogle Scholar
6. Censi, A., Franchi, A., Marchionni, L. and Oriolo, G., “Simultaneous calibration of Odometry and sensor parameters for mobile robots,” Robotics 29 (2), 475492 (2013).Google Scholar
7. Fabian, J. and Clayton, G. M., “Error analysis for visual odometry on indoor, wheeled mobile robots with 3-D sensors,” Mechatronics 19 (6), 18961906 (2014).Google Scholar
8. Correala, R., Pajaresa, G. and Ruzb, J., “Automatic expert system for 3D terrain reconstruction based on stereo vision and histogram matching,” Exp. Syst. Appl. 41 (4), 20432051 (2014).CrossRefGoogle Scholar
9. Dugarjav, B., Lee, S., Kim, D., Kim, J. and Chong, N., “Scan matching online cell decomposition for coverage path planning in an unknown environment,” Int. J. Precis. Eng. Manuf. 14 (9), 15511558 (2013).CrossRefGoogle Scholar
10. Kwon, H.1, Yousef, K. and Kak, A., “Building 3D visual maps of interior space with a new hierarchical sensor fusion architecture,” Robot. Auton. Syst. 61 (8), 749767 (2013).Google Scholar
11. Bojja, J., Kirkko-Jaakkola, M., Collin, J. and Takala, J., “Indoor localization methods using dead reckoning and 3D map matching,” J. Signal Process. Syst. 76 (3), 301312 (2013).CrossRefGoogle Scholar
12. Biswas, J. and Veloso, M., “Multi-sensor mobile robot localization for diverse environments,” Comput. Sci. 8371, 468479 (2014).Google Scholar
13. Blanco, J., González-Jiménez, J. and Fernández-Madrigal, J., “A robust, multi-hypothesis approach to matching occupancy grid maps,” Robotica 31 (5), 687701 (2013).CrossRefGoogle Scholar
14. Kim, S.-H., Jho, C.-W. and Hong, H.-K., “Automatic registration method for multiple 3D range data sets,” J. KISS: Software Appl. 30 (11,12), 12391246 (2003).Google Scholar
15. Szymon, R., and Levoy, M., “Efficient Variants of the ICP Algorithm,” Proceedings of the 3rd IEEE International Conference on 3-D Digital Imaging and Modeling, (2001) pp. 145–152.Google Scholar
16. Sabine, V. H., and Lemmerling, P., eds. Total Least Squares and Errors-In-Variables Modeling: Analysis, Algorithms and Applications (Springer Science & Business Media 2013).Google Scholar
17. Besl, P. J. and Mckay, N. D., “A method for registration of 3D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14 (2) 239256 (1992).Google Scholar
18. Zhang, Y., Park, J. H. and Chong, K.T., “Model algorithm control for path tracking of wheeled mobile robots,” Int. J. Precis. Eng. Manuf. 11 (5), 705714 (2010).CrossRefGoogle Scholar
19. Kim, J., Woo, S., Kim, J., Do, J., Kim, S. and Bae, S., “Inertial navigation system for an automatic guided vehicle with Mecanum wheels,” Int. J. Precis. Eng. Manuf. 13 (3), 379386 (2012).Google Scholar
20. Hsu, C., Chang, H. and Lu, Y., “Map Building of Unknown Environment using PSO-Tuned Enhanced Iterative Closest Point Algorithm,” Proceedings of the IEEE International Conference on System Science and Engineering (ICSSE), (Jul. 2013) pp. 279–284.CrossRefGoogle Scholar
21. Lee, Y., Song, J. and Choi, J., “Performance improvement of iterative closest point-based outdoor SLAM by rotation invariant descriptors of salient regions,” J. Intell. Robot. Syst. 71 (3–4), 349360 (2013).CrossRefGoogle Scholar
22. Zhang, Z., “Iterative point matching for registration of free-form curves and surfaces,” Int. J. Comput. Vis. 13 (2), 119152 (1994).Google Scholar
23. Arun, K. S., Huang, T. S. and Blostein, S. D., “Least square fitting of two 3-d point sets,” IEEE Trans. Pattern Anal. Mach. Intell. 9 (5), 698700 (1987).Google Scholar
24. Fuiita, T. and Kondo, Y., “3D Terrain Measurement System with Movable Laser Range Finder,” Proceedings of IEEE International Workshop on Safety, Security & Rescue Robotics SSRR, no. 2, (2009), pp. 1–6.Google Scholar
25. Ohno, K. and Tadokoro, S., “Dense 3D Map Building Based on LRF Data and Color Image Fusion,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, (2005) pp. 2792–2797.Google Scholar
26. Martín, F., Triebel, R., Moreno, L. and Siegwart, R., “Two different tools for three-dimensional mapping: DE-based scan matching and feature-based loop detection,” Robotica 32 (1), 1941 (2014).CrossRefGoogle Scholar
27. Surmann, H., Lingemann, K., Nuchter, A. and Hertzberg, J., “A 3D Laser Range for Autonomous Mobile Robots,” Proceedings of the 32nd International Symposium on Robotics ISR (Apr. 2001) pp. 153–158.Google Scholar
28. Oritin, D., Neira, J. and Montiel, J. M. M., “Relocation using Laser and Vision,” Proceedings of IEEE International Conference on Robotics and Automation, vol. 2 (2004) pp. 1505–1510.Google Scholar
29. Núñez, P., Vázquez-Martín, R., Bandera, A. and Sandoval, F., “Fast laser scan matching approach based on adaptive curvature estimation for mobile robots,” Robotica 27 (3), 469479 (2009).Google Scholar
30. Larsen, T. D. et al. “Incorporation of Time Delayed Measurements in a Discrete-Time Kalman Filter,” Proceedings of the 37th IEEE Conference on Decision and Control, vol. 4 (1998) pp. 3972–3977.Google Scholar
31. Bak, M. et al. “Location Estimation using Delayed Measurements,” Proceedings of the 5th IEEE International Workshop on Advanced Motion Control, Coimbra (1998).Google Scholar