Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T07:04:14.744Z Has data issue: false hasContentIssue false
  • English
  • Français

Mobile robot localization using the Hausdorff distance

Published online by Cambridge University Press:  01 March 2008

F. Donoso-Aguirre ,
J.-P. Bustos-Salas ,
M. Torres-Torriti  and
A. Guesalaga
F. Donoso-Aguirre
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
J.-P. Bustos-Salas
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
M. Torres-Torriti*
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
A. Guesalaga
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a novel method for localization of mobile robots in structured environments. The estimation of the position and orientation of the robot relies on the minimisation of the partial Hausdorff distance between ladar range measurements and a floor plan image of the building. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot's position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot, thus demonstrating that the approach provides an accurate, reliable and computationally feasible alternative for indoor robot localization and autonomous navigation.

Keywords

Mobile robot localizationHausdorff distanceMap-matching
Type
Article
Information
Robotica , Volume 26 , Issue 2 , March 2008 , pp. 129 - 141
Copyright
Copyright © Cambridge University Press 2007

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.Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, MA, 2005).Google Scholar
2.Dudek, G. D. and Jenkin, M., Computational Principles of Mobile Robotics (Cambridge University Press, Cambridge, UK, 2000).Google Scholar
3.Borenstein, J., Everett, H., Feng, L. and Wehe, D., “Mobile robot positioning: Sensors and techniques,” J. Robot. Syst. 14, 231–249 (1997).3.0.CO;2-R>CrossRefGoogle Scholar
4.Iyengar, S. and Elfes, A., Autonomous Mobile Robots (IEEE Computer Society, Los Alamitos, CA, 1991).Google Scholar
5.Elfes, A., “Sonar-based real-world mapping and navigation,” IEEE Trans. Robot. Autom. 3, 249–265 (1987).CrossRefGoogle Scholar
6.Leonard, J. J. and Durrant-Whyte, H. F., Directed Sonar Sensing for Mobile Robot Navigation (Kluwer Academic, Norwell, MA, 1992).CrossRefGoogle Scholar
7.Tardós, J. D., Neira, J., Newman, P. and Leonard, J., “Robust mapping and localization in indoor environments using sonar data,” Int. J. Robot. Res. 21 (4), 311–330 (2002).CrossRefGoogle Scholar
8.Cox, I. J., “Blanche: An experiment in guidance and navigation of an autonomous robot vehicle,” IEEE Trans. Robot. Autom. 7 (2), 193–203 (1991).CrossRefGoogle Scholar
9.Weiss, G. and Puttkamer, E., “A Map Based on Laserscans Without Geometric Interpretation,” Proceedings of the Intelligent Autonomous Systems. (1995) pp. 403–407.Google Scholar
10.Jensfelt, P. and Christensen, H., “Laser Based Position Acquisition and Tracking in an Indoor Environment,” Proceedings of the IEEE International Symposium on Robotics and Automation (1998) pp. 331–338.Google Scholar
11.DeSouza, G. N. and Kak, A. C., “Vision for mobile robot navigation: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. 24 (2), 237–267 (2002).CrossRefGoogle Scholar
12.Diosi, A. and Kleeman, L., “Advanced Sonar and Laser Range Finder Fusion for Simultaneous Localization and Mapping,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2004) pp. 1854–1859.Google Scholar
13.Castellanos, J. A., Neira, J. and Tardós, J. D., “Multisensor fusion for simultaneous localization and map building,” IEEE Trans. Robot. Autom. 17 (6), 908–914 (2001).CrossRefGoogle Scholar
14.Castellanos, J. A. and Tardós, J. D., Mobile Robot Localization and Map Building: A Multisensor fusion Approach (Kluwer Academic, Norwell, MA, 1999).CrossRefGoogle Scholar
15.Chatila, R. and Laumond, J.-P., “Position Referencing and Consistent World Modeling for Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation (1985) pp. 138–145.Google Scholar
16.Crowley, J. L., Wallner, F. and Schiele, B., “Position Estimation Using Principal Components of Range Data,” Proceedings of the IEEE International Conference on Robotics and Automation (1998) pp. 3128–3131.Google Scholar
17.Rucklidge, W. J., “Efficient visual recognition using the Hausdorff distance,” Int. J. Comput. Vis. 24, 251–270 (1997).CrossRefGoogle Scholar
18.Sim, D.-G. and Park, R.-H., “Two-dimensional object alignment based on robust oriented Hausdorff similarity measure,” IEEE Trans. Image Processing 10, 475–483 (2001).Google ScholarPubMed
19.Guesalaga, A., “Recursive estimation of radar biases using electronic charts,” IEEE Trans. Aerospace Electro. Syst. 40 (2), 725–733 (2004).CrossRefGoogle Scholar
20.Gutmann, J., Burgard, W., Fox, D. and Konolige, K., “An Experimental Comparison of Localization Methods,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (1998) pp. 736–743.Google Scholar
21.Filliat, D. and Meyer, J.-A., “Map-based navigation in mobile robots. I. A review of localization strategies,” Cogn. Syst. Res. 243–282 (2003).CrossRefGoogle Scholar
22.Olson, C. F., “Probabilistic self-localization for mobile robots,” IEEE Trans. Robot. Autom. 16 (1), 55–66 (2000).CrossRefGoogle Scholar
23.Talluri, R. and Aggarwal, J., “Positional Estimation Techniques for An Autonomous Mobile Robot — A Review,” In:Handbook of Pattern Recognition and Computer Vision (Chen, C. H., Pau, L. F. and Wang, P. S. P., eds.) (World Scientific Singapore, 1993) Ch. 4.4, pp. 769–801.CrossRefGoogle Scholar
24.Schiele, B. and Crowley, J. L., “A comparison of position estimation techniques using occupancy grids,” Robot. Autonom. Syst. 12, 163–171 (1994).CrossRefGoogle Scholar
25.Gutmann, J.-S. and Schlegel, C., “Amos: Comparison of Scan Matching Approaches for Self-localization in Indoor Environments,” Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots (1996) pp. 61–67.Google Scholar
26.Iocchi, L., Mastrantuono, D. and Nardi, D., “A Probabilistic Approach to Hough Localization,” Proceedings of the IEEE International Conference on Robotics and Automation (2001) pp. 4250–4255.Google Scholar
27.Nourbakhsh, I., Powers, R., and Birchfield, S., “DERVISH an office-navigating robot,” AI Mag. 16 (2), 53–60 (1995).Google Scholar
28.Simmons, R. and Koenig, S., “Probabilistic Robot Navigation in Partially Observable Environments,” Proceedings of the International Joint Conference on Artificial Intelligence (1995).Google Scholar
29.Kaelbling, L., Cassandra, A. and Kurien, J., “Acting Under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems (1996) pp. 963–972.Google Scholar
30.Thrun, S., “Bayesian landmark learning for mobile robot localization,” Mach. Learning 33 (1), 41–76 (1998).CrossRefGoogle Scholar
31.Roy, N. and Thrun, S., “Coastal navigation with mobile robots,” Adv. Neural Processing Syst. 12, 1043–1049 (1999).Google Scholar
32.Burgard, W., Fox, D., Hennig, D. and Schmidt, T., “Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids,” Proceedings of the 14th National Conference on Artificial Intelligence (1996) pp. 896–901.Google Scholar
33.Burgard, W., Derr, A., Fox, D. and Cremers, A. B., “Integrating Global Position Estimation and Position Tracking for Mobile Robots: The Dynamic Markov Localization Approach,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (1998) pp. 730–735.Google Scholar
34.Fox, D., Burgard, W. and Thrun, S., “Markov localization for mobile robots in dynamic environments,” J. Artif. Intell. Res. 11, 391–427 (1999).CrossRefGoogle Scholar
35.Dellaert, F., Fox, D., Burgard, W. and Thrun, S., “Monte Carlo Localization for Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation (1999) pp. 1322–1328.Google Scholar
36.Fox, D., Thrun, S., Dellaert, F. and Burgard, W., “Particle Filters for Mobile Robot Localization,” In:Sequential Monte Carlo in Practice (Doucet, A., Freitas, N. de and Gordon, N., eds.) (Springer-Verlag, Berlin, Germany, 2001).Google Scholar
37.Dellaert, F., “A Sample of Monte Carlo Methods in Robotics and Vision,” Proceedings of the ISM International Symposium on the Science of Modeling — The 30th Anniversary of the Information Criterion (2003) pp. 211–222.Google Scholar
38.Gordon, N. J., Salmond, D. J. and Smith, A. F. M., “Novel approach to nonlinear/Non-Gaussian Bayesian state estimation,” IEE Proc. F 140 (2), 107–113 (1993).Google Scholar
39.Kitagawa, G., “Monte Carlo filter and smoother for non-Gaussian nonlinear state space models,” J. Comput. Graphical Statist. 5 (1), 1–25 (1996).Google Scholar
40.Isard, M. and Blake, A., “Contour Tracking by Stochastic Propagation of Conditional Density,” Proceedings of European Conference on Computer Vision (1996) pp. 343–356.Google Scholar
41.Isard, M. and Blake, A., “Condensation–conditional density propagation for visual tracking,” Int. J. Comput. Vis. 29 (1), 5–28 (1998).CrossRefGoogle Scholar
42.Ristic, B., Arulampalam, S. and Gordon, N., Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Norwood, MA, 2004).Google Scholar
43.Moravec, H. and Elfes, A., “High Resolution Maps from Wide Angle Sonar,” Proceedings of the IEEE International Conference on Robotics and Automation (1985) pp. 116–121.Google Scholar
44.Jensfelt, P. and Kristensen, S., “Active global localisation for a mobile robot using multiple hypothesis tracking,” IEEE Trans. Robot. Autom. 17 (5), 748–760 (2001).CrossRefGoogle Scholar
45.Gutmann, J.-S. and Fox, D., “An Experimental Comparison of Localization Methods Continued,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2002) pp. 454–459.Google Scholar
46.Kristensen, S. and Jensfelt, P., “An Experimental Comparison of Localisation Methods, the MHL Sessions,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2003) pp. 992–997.Google Scholar
47.Schaffer, G., Gonzalez, J. and Stentz, A., “Comparison of Two Range-Based Pose Estimators for a Mobile Robot,” Proceedings of the 1992 SPIE Conference on Mobile Robots (1992) pp. 661–667.Google Scholar
48.Marchetti, L., Grisetti, G. and Iocchi, L., “A Comparative Analysis of Particle Filter Based Localization Methods,” Proc. of RoboCup Symposium (2006).Google Scholar
49.Veltkamp, R. C. and Hagerdoorn, M., “State of the Art in Shape Matching,” In:Principles of Visual Information Retrieval (Advances in Pattern Recognition) (Lew, M. S. ed.) (Springer, Berlin, Germany, 2006) pp. 87–115.Google Scholar
50.Lu, F. and Milios, E E, “Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1994) pp. 935–938.Google Scholar
51.Lu, F. and Milios, E., “Globally consistent range scan alignment for environment mapping,” Autonom. Robots 4, 333–349 (1997).CrossRefGoogle Scholar
52.Bailey, T. and Nebot, E., “Localisation in large-scale environments,” Robot. Autonom. Syst. 37 (4), 261–281 (2001).CrossRefGoogle Scholar
53.Pfister, S. T., Kreichbaum, K. L., Roumeliotis, S. I. and Burdick, J.W., “Weighted range Sensor Matching Algorithms for Mobile Robot Displacement Estimation,” Proceedings of the IEEE International Conference on Robotics and Automation (2002) pp. 1667–1674.Google Scholar
54.Minguez, J., Lamiraux, F. and Montesano, L., “Metric-Based Scan Matching Algorithms for Mobile Robot Displacement Estimation,” Proceedings of the IEEE International Conference on Robotics and Automation (2005) pp. 3557–3563.Google Scholar
55.Besl, P. J. and McKay, N. D., “A method for registration of 3-D shapes,” IEEE Trans. Pattern. Anal. Mach. Intell. 14 (2), 239–256 (1992).CrossRefGoogle Scholar
56.Zhang, Z., “Iterative point matching for registration of free-form curves and surfaces,” Int. J. Comput. Vis. 13 (2), 119–152 (1994).CrossRefGoogle Scholar
57.Rusinkiewicz, S. and Levoy, M., “Efficient Variants of the ICP Algorithm,” Proceedings of the International Conference on 3D Digital Imaging and Modeling (2001) pp. 145–152.Google Scholar
58.Liu, Y., “Improving ICP with easy implementation for free form surface matching,” Pattern Recog. 37, 211–226 (2004).CrossRefGoogle Scholar
59.Diosi, A. and Kleeman, L., “Laser Scan Matching in Polar Coordinates with Application to SLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2005) pp. 3317–3322.Google Scholar
60.Weiss, G., Wetzler, C. and Puttkamer, E., “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 (1994) pp. 595–601.Google Scholar
61.Schultz, A. C. and Adams, W., “Continuous localization using evidence grids,” Proceedings of the IEEE International Conference on Robotics and Automation (1998) pp. 2833–2839.Google Scholar
62.Konolige, K. and Chou, K., “Markov localization using correlation,” Proceedings of the International Joint Conference on Articial Intelligence (1999) pp. 1154–1159.Google Scholar
63.Zhang, L. and Ghosh, B. K., “Line Segment Based Map Building and Localization Using 2D Laser Rangefinder,” Proceedings of the IEEE International Conference on Robotics and Automation (2000) pp. 2538–2543.Google Scholar
64.Borges, G. A. and Aldon, M.-J., “Line extraction in 2D range images for mobile robotics,” J. Intell. Robot. Syst. 40, 267–297 (2004).CrossRefGoogle Scholar
65.Nguyen, V., Martinelli, A., Tomatis, N. and Siegwart, R., “A Comparison of Line Extraction Algorithms Using 2D Laser Rangefinder for Indoor Mobile Robotics,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2005) pp. 1929–1934.Google Scholar
66.Bertsekas, D. P., Nonlinear Programming 2nd ed. (Athena Scientific, Belmont, MA, 2004).Google Scholar
67.Huttenlocher, D. P., Klanderman, G. A. and Rucklidge, W. J., “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 15 (9), (1993) 850–863.CrossRefGoogle Scholar
68.Kwon, O., Sim, D.-G. and Park, R.-H.. “Nonparametric hierarchical Hausdorff distance matching algorithm,” Opt. Eng. 39, 1917–1927, (2000).CrossRefGoogle Scholar
69.Taylor, D. W., Johnson, P. N. and Faulkner, W. T., “Local Area Radio Navigation: A Tool for GPS-denied Geolocation,” Proceedings of the SPIE-Aerosense Conf. (2003) Vol. 5084, pp. 125–136.Google Scholar