Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-10T20:08:39.816Z Has data issue: false hasContentIssue false
  • English
  • Français

Active global localization based on localizability for mobile robots

Published online by Cambridge University Press:  25 April 2014

Yong Wang ,
Weidong Chen ,
Jingchuan Wang  and
Hesheng Wang
Yong Wang
Affiliation:
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China State Key Laboratory of Robotics and System (HIT), Harbin 150001, China
Weidong Chen*
Affiliation:
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China State Key Laboratory of Robotics and System (HIT), Harbin 150001, China
Jingchuan Wang
Affiliation:
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China State Key Laboratory of Robotics and System (HIT), Harbin 150001, China
Hesheng Wang
Affiliation:
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China State Key Laboratory of Robotics and System (HIT), Harbin 150001, China
*
*Corresponding author. E-mail: wdchen@sjtu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Summary

In global localization under the framework of a particle filter, the acquiring of effective observations of the whole particle system will be greatly effected by the uncertainty of a prior-map, such as unspecific structures and noises. In this study, taking the uncertainty of the prior-map into account, a localizability-based action selection mechanism for mobile robots is proposed to accelerate the convergence of global localization. Localizability is defined to evaluate the observations according to the prior-map (probabilistic grid map) and observation (laser range-finder) models based on the Cramér-Rao Bound. The evaluation considers the uncertainty of the prior-map and does not need to extract any specific observation features. Essentially, localizability is the determinant of the inverse covariance matrix for localization. Specifically, at the beginning of every filtering step, the action, which makes the whole particle system to achieve the maximum localizability distinctness, is selected as the actual action. Then there will be the increased opportunities for accelerating the convergence of the particles, especially in the face of the prior-map with uncertainty. Additionally, the computational complexity of the proposed algorithm does not increase significantly, as the localizability is pre-cached off-line. In simulations, the proposed active algorithm is compared with the passive algorithm (i.e. global localization with the random robot actions) in environments with different degrees of uncertainty. In experiments, the effectiveness of the localizability is verified and then the comparative experiments are conducted based on an intelligent wheelchair platform in a real environment. Finally, the experimental results are compared and analyzed among the existing active algorithms. The results demonstrate that the proposed algorithm could accelerate the convergence of global localization and enhance the robustness against the system ambiguities, thereby reducing the failure probability of the convergence.

Keywords

Active global localizationQuality of mapLocalizabilityAction selection for mobile robotsParticle filter
Type
Articles
Information
Robotica , Volume 33 , Issue 8 , October 2015 , pp. 1609 - 1627
Copyright
Copyright © Cambridge University Press 2014 

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. Borenstein, J., Everett, B. and Feng, L., Navigating Mobile Robots: Systems and Techniques (A. K. Peters, Ltd., Wellesley, MA, 1996).Google Scholar
2. Weiss, G., Wetzler, C. and Puttkamer, E. V., “Keeping Track of Position and Orientation of Moving Indoor Systems by Correlation of Range-Finder Scans,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Munich, Germany (September 12–16, 1994) pp. 595–601.Google Scholar
3. Wang, Y. and Chen, W. D., “Hybrid Map-Based Navigation for Intelligent Wheelchair,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (May 9–13, 2011) pp. 637–642.Google Scholar
4. Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, MA, 2005).Google Scholar
5. Burgard, W., Fox, D., Hennig, D. and Schmidt, T., “Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids,” Proceedings of the National Conference on Artificial Intelligence (AAAI), Portland, USA (August 4–8, 1996) pp. 896–901.Google Scholar
6. Jensfelt, P. and Kristensen, S., “Active global localisation for a mobile robot using multiple hypothesis tracking,” IEEE Tran. Robot. Autom. 17 (5), 748–760 (2001).Google Scholar
7. Thrun, S., Fox, D., Burgard, W. and Dellaert, F., “Robust Monte Carlo localization for mobile robots,” Artif. Intell. 128 (1–2), 99–141 (2001).Google Scholar
8. Menegatti, E., Zoccarato, M., Pagello, E. and Ishiguro, H., “Image-based Monte Carlo localisation with omnidirectional images,” Robot. Auton. Syst. 47 (4), 251–267 (2004).Google Scholar
9. Lin, H. H. and Tsai, C. C., “Improved global localization of an indoor mobile robot via fuzzy extended information filtering,” Robotica 26, 241–254 (2008).Google Scholar
10. Rao, M., Dudek, G. and Whitesides, S., “Randomized algorithms for minimum distance localization,” Int. J. Robot. Res. 26, 917–933 (2007).Google Scholar
11. O'Kane, J. M., “Global Localization Using Odometry,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Orlando, USA (May 15–19, 2006) pp. 37–42.Google Scholar
12. He, T. and Hirose, S., “A global localization approach based on line-segment relationmatching technique,” Robot. Auton. Syst. 60 (1), 95–112 (2012).Google Scholar
13. Zhang, L., Zapata, R. and Lépinay, P., “Self-adaptive Monte Carlo localization for mobile robots using range finders,” Robotica 30, 229–244 (2012).Google Scholar
14. Cen, G. H., Matsuhira, N., Hirokawa, J., Ogawa, H. and Hagiwara, I.. “Mobile Robot Global Localization Using Particle Filters,” Proceedings of the IEEE International Conference on Control, Automation and Systems (ICCAS), Seoul, Korea (October 14–17, 2008) pp. 710–713.Google Scholar
15. Tovey, C. and Koenig, S., “Localization: Approximation and performance bounds to minimize travel distance,” IEEE Tran. Robot. 26 (2), 320–330 (2010).Google Scholar
16. Hughes, K. and Murphy, R. R., “Ultrasonic Robot Localization Using Dempster-Shafer Theory,” Proceedings of the SPIE Stochastic Methods in Signal Processing, Image Processing, and Computer Vision, Invited Session on Applications for Vision and Robotics, Society of Photo Optical, San Diego, USA (July 19–23, 1992) pp. 2–11.Google Scholar
17. Fox, D., Burgard, W. and Thrun, S., “Markov localization for mobile robots in dynamic environments,” J. Artif. Intell. Res. 11, 391–427 (1999).Google Scholar
18. Kwo, T., Yang, J., Song, J. and Chung, W., “Efficiency Improvement in Monte Carlo Localization through Topological Information,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijing, China (October 9–15, 2006) pp. 424–429.Google Scholar
19. Radacina Rusu, S., Hayes, M. J. D. and Marshall, J. A., “Localization in Large-Scale Underground Environments with RFID,” Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Niagara Falls, Canada (May 8–11, 2011) pp. 1140–1143.Google Scholar
20. He, T. and Hirose, S., “Observation-driven Bayesian filtering for global location estimation in the field area,” J. Field Robot. 30, 489–518 (2013).Google Scholar
21. Crisan, D. and Doucet, A., “A survey of convergence results on particle filtering methods for practitioners,” IEEE Tran. Signal Process. 50 (3), 736–746 (2002).Google Scholar
22. Fox, D., Burgard, W. and Thrun, S., “Active Markov localization for mobile robots,” Robot. Autom. Syst. 25 (3–4), 195–207 (1998).Google Scholar
23. Doucet, A., On Sequential Simulation-Based Methods for Bayesian Filtering (Technical Report, Signal Processing Group, Department of Engineering, University of Cambridge, 1998).Google Scholar
24. Porta, J. M., Verbeek, J. J. and Krose, B. J. A., “Active appearance-based robot localization using stereo vision,” Auton. Robots 18 (1), 59–80 (2005).Google Scholar
25. Murtra, A. C., Tur, J. M. M. and Sanfeliu, A., “Action evaluation for mobile robot global localization in cooperative environments,” Robot. Auton. Syst. 56 (10), 807–818 (2008).Google Scholar
26. Zhou, H. J. and Sakane, S., “Sensor planning for mobile robot localization - A hierarchical approach using a bayesian network and a particle filter,” IEEE Tran. Robot. 24 (2), 481–487 (2008).Google Scholar
27. Gonzalez, J. P. and Stentz, A. T., “Planning with Uncertainty in Position: An Optimal and Efficient Planner,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Edmonton, Canada (August 2–6, 2005) pp. 2435–2442.Google Scholar
28. Censi, A., “An Accurate Closed-Form Estimate of ICP's Covariance,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Roma, Italy (April 10–14, 2007) pp. 3167–3172.Google Scholar
29. Censi, A., Calisi, D., de Luca, A. and Oriolo, G., “A Bayesian Framework for Optimal Motion Planning with Uncertainty,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, USA (May 19–23, 2008) pp. 1798–1805.Google Scholar
30. Censi, A., Franchi, A., Marchionni, L. and Oriolo, G., “Simultaneous calibration of odometry and sensor parameters for mobile robots,” IEEE Tran. Robot. (2013) to appear.Google Scholar
31. Wang, Y., Chen, W. D., Wang, J. C. and Wang, W., “Improved particle filter localization in crowed environments for mobile robots,” Robotica 34 (5), 596–603 (2012).Google Scholar
32. Liu, Z., Chen, W. D., Wang, Y. and Wang, J. C., “Localizability Estimation for Mobile Robots Based on Probabilistic Grid Map and its Applications to Localization,” Proceedings of the IEEE International Conference on Multisensor Fusion and Information Integration (MFI), Hamburg, Germany (September 13–15, 2012) pp. 46–51.Google Scholar
33. Wang, Y., Chen, W. D., Wang, J. C. and Wang, H. S., “Action Selection Based on Localizability for Active Global Localization of Mobile Robots,” Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), Chengdu, China (August 5–8, 2012) pp. 2071–2076.Google Scholar
34. Bar-Shalom, Y., Tracking and Data Association (Academic Press Professional, San Diego, USA, 1987).Google Scholar
35. Simmons, R., Goldberg, D., Goode, A., Montemerlo, M., Roy, N., Schultz, A. C., Abramson, M., Horswill, I., Kortenkamp, D. and Maxwell, B., “Grace: An autonomous robot for the AAAI robot challenge,” AI Magazine 24 (2), 51–72 (2003).Google Scholar
36. Bobrovsky, B. Z. and Zakai, M., “A lower bound on the estimation error for markov processes,” IEEE Tran. Autom. Control 20 (6), 785–788 (1975).Google Scholar
37. Censi, A., “On Achievable Accuracy for Range-Finder Localization,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Roma, Italy (April 10–14, 2007) pp. 4170–4175.Google Scholar
38. Bar-Shalom, Y., Li, X. R. and Kirubarajan, T., Estimation with Applications to Tracking and Navigation (John Wiley & Sons, Inc., New York, 2001).Google Scholar
39. Gray, R. M. and Neuhoff, D. L., “A history of k-means type of algorithms,” IEEE Tran. Inf. Theory 44, 2325–2384 (1998).Google Scholar
40. Antonelli, G., Chiaverini, S. and Fusco, G., “A calibration method for odometry of mobile robots based on the least-squares technique: Theory and experimental validation,” IEEE Tran. Robot. 21 (5), 994–1004 (2005).Google Scholar
41. Montemerlo, M., Roy, N. and Thrun, S., “Perspectives on Standardization in Mobile Robot Programming: The Carnegie Mellon Navigation (CARMEN) Toolkit,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA (October 27–31, 2003) pp. 2436–2441.Google Scholar
42. De Maesschalck, R., Jouan-Rimbaud, D. and Massart, D. L., “The Mahalanobis distance,” Chemometr. Intell. Lab. Syst. 50 (1), 1–18 (2000).Google Scholar