Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-07T16:35:21.841Z Has data issue: false hasContentIssue false
  • English
  • Français

Outdoor mapping and localization using satellite images

Published online by Cambridge University Press:  15 January 2010

C. U. Dogruer ,
A. B. Koku  and
M. Dolen
C. U. Dogruer*
Affiliation:
Mechanical Engineering Department, Hacettepe University, Beytepe Campus, 06800 Beytepe/Ankara, Turkey
A. B. Koku
Affiliation:
Mechanical Engineering Department, Hacettepe University, Beytepe Campus, 06800 Beytepe/Ankara, Turkey
M. Dolen
Affiliation:
Mechanical Engineering Department, Hacettepe University, Beytepe Campus, 06800 Beytepe/Ankara, Turkey
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Recently, satellite images of most urban settings has become available on the internet. In this study, a novel mapping and global localization approach, which uses these images, is proposed for outdoor mobile robots operating in urban environment. The mapping of large-scale outdoor environments is done by employing the satellite images acquired by remote sensing technology, and then a map-based approach, that is, Monte Carlo localization is used for localization. The novelty of proposed method is that it uses standard equipment present on almost all autonomous robots and satellite images thus it acts as an alternative to GPS data in urban environments. Extensive field tests are presented to demonstrate the effectiveness of proposed approach.

Keywords

MappingLocalizationOutdoor mobile robotsSatellite imagesRemote sensing
Type
Article
Information
Robotica , Volume 28 , Issue 7 , December 2010 , pp. 1001 - 1012
Copyright
Copyright © Cambridge University Press 2010

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., Fox, D., Burgard, W. and Dellaert, F., “Robust monte carlo localization for mobile robots,” Artif. Intell. 128, 99–141 (2001).Google Scholar
2.Fox, D., Markov Localization: A Probabilistic Framework for Mobile Robot Localization and Navigation Ph.D. Dissertation (Bonn, Germany: Institute of Computer Science III, University of Bonn, 1998).Google Scholar
3.Fox, D., “Adapting the sampling size in particle filters through KLD-sampling,” Int. J. Robot. Res. 22 (12), 985–1003 (2003).Google Scholar
4.Leonard, J. J. and Durrant-Whyte, H. F., “Mobile robot localization by tracking geometric beacons,” IEEE Trans. Robot. Autom. 7 (3), 376–382 (1991).CrossRefGoogle Scholar
5.Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H. F. and Csorba, M., “A solution to the simultaneous localization and map building (SLAM) problem,” IEEE Trans. Robot. Autom. 17 (3), 229–241 (2001).Google Scholar
6.Guivant, J., Nebot, E. and Baiker, S, “Localization and map building using laser range sensors in outdoor applications,” J. Robot. Syst. 17 (3), 565–583 (2000).Google Scholar
7.Guivant, J. E. and Nebot, E., “Optimization of the simultaneous localization and map-building algorithms for real time implementation,” IEEE Trans. Robot. Autom. 17 (3), 242–257 (2001).Google Scholar
8.Smith, R., Self, M. and Cheeseman, P., “Estimating Uncertain Spatial Relationships in Robotics,” In: Autonomous Robot Vehicles (Cox, Ingemar J. and Wilfong, Gordon T., eds.) (Springer-Verlag, New York, 1990) pp. 167–193.Google Scholar
9.Montemerlo, M. and Thrun, S., “Simultaneous Localization and Mapping With Unknown Data Association Using FastSLAM,” IEEE International Conference on Robotics and Automation, Proceedings. ICRA ‘03. 2 (Taipei, Taiwan, 2003) pp. 1985–1991.Google Scholar
10.Thrun, S., Koller, D., Ghahramani, Z, Durrant-Whyte, H. and Ng, A. Y., “Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results,” In: Springer Tracts in Advanced Robotics 7, Algorithmic Foundations of Robotics V (Bossonnat, J. D., Burdick, J., Goldberg, K., Huthinson, S, eds.) (Springer, 2003) pp. 363–381.Google Scholar
11.Masson, F., Guivant, J. and Nebot, E., “Robust navigation and mapping architecture for large environments,” J. Robot. Syst. 20 (10), 621–634 (2003).Google Scholar
12.Eliazar, A. and Parr, R., “DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks,” Proceedings of the International Conference on Artificial Intelligence (IJCAI) (Acapulco, Mexico, 2003).Google Scholar
13.Neira, J. and Tardos, J. D., “Data association in stochastic mapping using the joint compatibility test,” IEEE Trans. Robot. Autom. 17 (6), 890–897 (2001).CrossRefGoogle Scholar
14.Borenstein, J. and Feng, L., “UMBmark: A benchmark test for measuring odometry errors in mobile robots,” SPIE Conference on Mobile Robots, Philadelphia, PA (1995) pp. 22–26.Google Scholar
15.Google Earth Software, @ Google, [Online]. Available http://earth.google.comGoogle Scholar
16.Dogruer, C. U., Koku, B. and Dolen, M., “Global Urban Localization of an Outdoor Mobile Robot with Genetic Algorithms,” In: Springer Tracts in Advanced Robotics, European Robotics Symposium 2008 (Herman, Bruyninckx; Libor, Preucil and Miroslav, Kulich, eds.) (Springer Berlin/Heidelberg 44, 2008) pp. 103–112.Google Scholar
17.Dogruer, C. U., Koku, B. and Dolen, M., “A Novel Soft Computing Algorithm to Segment Satellite Images for Mobile Robot Localization and Navigation,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 07, San Diego, CA (2007) pp. 2077–2082.Google Scholar
18.Dogruer, C. U., Koku, B. and Dolen, M., “Global Urban Localization of Outdoor Mobile Robots Using Satellite Images,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 08, Nice, France (2008) pp. 3927–3932.Google Scholar
19.Csorba, M., Uhlmann, J. K. and Durrant-Whyte, H., “A Suboptimal Algorithm for Automatic Map Building,” Proceedings of the American control conference, Albuquerque, New Mexico (1997) pp. 537–541.Google Scholar
20.Martinelli, A., Nguyen, V., Tomatis, N. and Siegwart, R., “A relative approach to SLAM based on shift and rotation invariants,” Robot. Auton. Syst. 55, 50–61 (2007).Google Scholar
21.Newman, P. M., On the Structure and Solution of Simultaneous Localization and Mapping Problem Ph.D. Dissertation (Sydney, Australia: Australian Centre for Filed Robotics, The University of Sydney, 1999).Google Scholar
22.Wang, Z., Huang, S. and Dissanayeke, G., “D-SLAM: A decoupled solution to simultaneous localization and mapping,” Int. J. Robot. Res. 26 (2), 187–204 (2007).Google Scholar
23.Dissanayake, G., Williams, S. B., Durrant-Whyte, H. and Bailey, T., “Map management for efficient simultaneous localization and mapping (SLAM),” Auton. Robot. 12, 267–286 (2002).Google Scholar
24.Nguyen, V., Harati, A., Martinelli, A. and Siegwart, R., “Orthogonal SLAM: A Step Toward Lightweight Indoor Autonomous Navigation,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 06, Bejing, China (2006) pp. 5007–5012.Google Scholar
25.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Eighteenth National Conference on Artificial Intelligence (Edmonton, Alberta, Canada, 2002) pp. 593–598.Google Scholar
26.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM 2.0,” IJCAI, Acapulco, Mexico (2003) pp. 1151–1156.Google Scholar
27.Eliazar, A. and Parr, R., “DP-SLAM 2.0,” Proceedings – IEEE International Conference on Robotics and Automation (New Orleans, LA, USA, 2004) pp. 1314–1320.Google Scholar
28.Eliazar, A. and Parr, R., “Hierarchical linear/constant time slam using particle filters for dense maps,” Adv. Neural Inf. Process. Syst. (2005).Google Scholar
29.Estrada, C., Neira, J. and Tardos, J. D., “Hierarchical SLAM: Real-time accurate mapping of large environments,” IEEE Trans. Robot. 21 (4), 588–596 (2005).Google Scholar
30.Guivant, J., Nebot, E., Nieto, J. and Masson, F., “Navigation and mapping in large unstructured environments,” Int. J. Robot. Res. 23, 449–472 (2004).Google Scholar
31.Nieto, J., Guivant, J. and Nebot, E., “DenseSLAM: Simultaneous localization and dense mapping,” Int. J. Robot. Res. 25 (8), 711–744 (2006).CrossRefGoogle Scholar
32.Madhavan, R. and Durrant-Whyte, H. F., “Natural landmark-based autonomous vehicle navigation,” Robot. Auton. Syst. 46, 7–95 (2004).Google Scholar
33.Booker, G., “Correlation of Millimeter Wave Radar Images with Aerial Photographs for Aautonomous Navigation UAV,” Second International Conference on Sensing Technology, Palmerstone North, New Zealand (2007) pp. 529–533.Google Scholar
34.Guivant, J. and Katz, R., “Global Urban Localization Based on Road Maps,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 07, San Diego, CA (2007) pp. 1079–1084.Google Scholar
35.Isaev, A. S., Korovin, G. N., Bartalev, S. A., Ershov, D. V., Janetos, , Kasischke, E. S., Shugart, H. H., French, N. H. F., Orlick, B. E. and Murphy, T. L. “Using remote sensing to assess Russian forest fire carbon emissions,” Clim. Change 55, 235–249 (2002).Google Scholar
36.Park, P. K., Elrod, J. A. and Kester, D. R., “Applications of satellite remote sensing to marine pollution studies,” Chem. Ecol. 5, 57–73 (1991).CrossRefGoogle Scholar
37.Shin, D. and Lee, K., “Use of remote sensing and geographical information systems to estimate green space surface temperature change as a result of urban expansion,” Landscape Ecol. Eng. 1, 169–176 (2005).Google Scholar
38.Weng, Q. and Yang, S., “Urban air pollution patterns, land use, and thermal landscape: an examination of linkage using GIS,” Environ. Monit. Assess. 117, 463–489 (2006).Google Scholar
39.Gayan, J. and Watts, C. J., “The use of remote sensing for estimating et of irrigated wheat and cotton in Northwest Mexico,” Irrig. Drainage Syst. 19, 301–320 (2005).Google Scholar
40.Patel, N. R., Bhattacharjee, B., Mohammed, A. J., Tanupriya, B. and Saha, S. K., “Remote sensing of regional yield assessment of wheat in Haryana, India,” Int. J. Remote Sens. 19, 4071–4090 (2006).Google Scholar
41.Sarup, J., Muthukumaran, M., Mathur, N. and Peshwa, V., “Study on tectonics in relation to the seismic activity of dalvat area, Nasaik district, Maharashtra, India using remote sensing and GIS techniques,” Int. J. Remote Sens. 27, 2371–2387 (2006).Google Scholar
42.Stramondo, S., Bignami, C., Chini, M., Pierdiccai, N. and Tertulliani, A., “Satellite radar and optical remote sensing for earthquake damage detection: Results from different case studies,” Int. J. Remote Sens. 20, 4433–4447 (2006).CrossRefGoogle Scholar
43.Barnes, I., “Aerial remote sensing techniques used in the managements of archeological monuments on the British army's salisbury plain training area, Wiltshire, UK,” Archeol. Pospect. 10, 83–90 (2003).CrossRefGoogle Scholar
44.Harrower, M., McCorriston, J. and Oches, E. A., “Mapping the roots of agriculture in Southern Arabia: The application of satellite remote sensing, global positioning system and geographic information system technologies,” Archeol. Prospect. 9, 35–42 (2002).Google Scholar
45.Khan, N. I. and Islam, A., “Quantification of erosion patterns in The Brahmaputra–Jamuna river using geographical information system and remote sensing technology,” Hydrol. Process. 17, 959–966 (2003).Google Scholar
46.Pietroniro, A. and Prowse, T. D., “Applications of remote sensing in hydrology,” Hydrol. Process. 16, 1537–1541 (2002).Google Scholar
47.Richards, J. A. and Jia, X., Remote Sensing Digital Image Analysis, 4th ed. (Springer-Verlag, Berlin, Germany, 2006).Google Scholar
48.Hertz, J., Krogh, A. and Palmer, R. G., Introduction to the Theory of Neural Computation, Santa Fe Institute Studies In The Sciences Of Complexity Lecture Notes, Vol. 1 (Westview Press, 1991).Google Scholar
49.Czogala, E. and Leski, J., Fuzzy and Neuro-fuzzy Intelligent Systems (Studies in Fuzziness and Soft Computing) (Physica-Verlag, Heidelberg, Germany, 2000).Google Scholar
50.Yen, J. and Langari, R., Fuzzy Logic : Intelligence, Control and Information (Prentice Hall, USA, 1999).Google Scholar
51.Dogruer, C. U., Global Urban Localization of Outdoor Mobile Robots Using Satellite Images Ph.D. Dissertation (Ankara, Turkey: Middle East Technical University, Feb. 2009).Google Scholar
52.Doucet, A., Freitas, N. and Gordon, N., Sequential Monte Carlo methods in practice (Doucet, A., Freitas, N. and Gordon, N., eds.) (Springer, 2001) pp. 3–13.CrossRefGoogle Scholar
53.Gordon, N., Salmond, D. J. and Smith, A. F. M., “Novel approach to nonlinear/non-gaussian bayesian state estimation,” IEEE Proc.-F 140 (2), 107–113 (1993).Google Scholar