Hostname: page-component-89b8bd64d-7zcd7 Total loading time: 0 Render date: 2026-05-06T18:11:47.645Z Has data issue: false hasContentIssue false
  • English
  • Français

Landmark detection and localization for mobile robot applications: a multisensor approach

Published online by Cambridge University Press:  11 August 2009

Dilan Amarasinghe ,
George K. I. Mann  and
Raymond G. Gosine
Dilan Amarasinghe*
Affiliation:
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, Canada, A1B 3X5.
George K. I. Mann
Affiliation:
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, Canada, A1B 3X5.
Raymond G. Gosine
Affiliation:
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, Canada, A1B 3X5.
*
*Corresponding author. Email: amarasin@engr.mun.ca
Get access Rights & Permissions [Opens in a new window]

Summary

This paper describes a landmark detection and localization using an integrated laser-camera sensor. Laser range finder can be used to detect landmarks that are direction invariant in the laser data such as protruding edges in walls, edges of tables, and chairs. When such features are unavailable, the dependant processes will fail to function. However, in many instances, larger number of landmarks can be detected using computer vision. In the proposed method, camera is used to detect landmarks while the location of the landmark is measured by the laser range finder using laser-camera calibration information. Thus, the proposed method exploits the beneficial aspects of each sensor to overcome the disadvantages of the other sensor. While highlighting the drawbacks and limitations of single sensor based methods, an experimental results and important statistics are provided for the verification of the affectiveness sensor fusion method using Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) as an example application.

Keywords

Sensor fusionLandmark detectionLandmark localizationSimultaneous localization and mappingMobile robotics

Information

Type
Article
Information
Robotica , Volume 28 , Issue 5 , September 2010 , pp. 663 - 673
Copyright
Copyright © Cambridge University Press 2009

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.)

Article purchase

Temporarily unavailable

References

1.Guivant, J. E., Masson, F. R. and Nebot, E. M., “Simultaneous localization and map building using natural features and absolute information,” Robot. Auton. Syst., 40 (2), 79–90 (2002).CrossRefGoogle Scholar
2.Jeong, W. Y. and Lee, K. M., “Visual SLAM with Line and Corner Features,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 2006) pp. 2570–2575.Google Scholar
3.Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F. and Sayd, P., “Monocular Vision Based SLAM for Mobile Robots,” Proceedings of International Conference on Pattern Recognition, Hong Kong (Aug. 2006) pp. 1027–1031.Google Scholar
4.Miro, J. V., Dissanayake, G. and Zhou, W., “Vision-Based Slam Using Natural Features in Indoor Environments,” Proceedings of International Conference on Intelligent Sensors, Sensor Networks and Information Processing (Dec. 2005) pp. 151–156.CrossRefGoogle Scholar
5.Rybski, P. E., Roumeliotis, S. I., Gini, M. and Papanikolopoulos, N., “Appearance-Based Minimalistic Metric Slam,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, USA (Oct. 2003) pp. 194–199.Google Scholar
6.Porta, J. M. and Krose, B. J. A., “Appearance-Based Concurrent Map Building and Localization Using a Multi-Hypotheses Tracker,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (Oct. 2004) pp. 3424–3429.Google Scholar
7.Gross, H. M., Koenig, A. and Mueller, St., “Omniview-Based Concurrent Map Building and Localization Using Adaptive Appearance Maps,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Hawaii, USA (Oct. 2005) pp. 3510–3515.Google Scholar
8.Porta, J. M., Verbeek, J. J. and Krose, B., “Enhancing Appearance-Based Robot Localization Using Sparse Disparity Maps,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, USA (Oct. 2003) pp. 980–985.Google Scholar
9.Krose, B. J. A., Vlassis, N. and Bunschoten, R., “A probabilistic model for appearance-based robot localization,” Image Vis. Comput., 12 (6), 381–391 (Apr. 2001).CrossRefGoogle Scholar
10.Ulrich, I. and Nourbakhsh, I., “Appearance-Based Place Recognition for Topological Localization,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, USA (Apr. 2000) pp. 1023–1029.Google Scholar
11.Davison, A. J., Mobile Robot Navigation Using Active Vision. PhD thesis (Oxford, UK: University of Oxford, 1998).Google Scholar
12.Davison, A. J. and Murray, D. W., “Simultaneous localization and map-building using active vision,” IEEE Trans. Pattern Anal. Mach. Intell., 24 (7), 865–880 (2002).CrossRefGoogle Scholar
13.Saeedi, P., Lawrence, P. D. and Lowe, D. G., “Vision-based 3d trajectory tracking for unknown environments,” IEEE Trans. Robot., 22 (1), 119–136 (Feb. 2006).CrossRefGoogle Scholar
14.Se, S. and Jasiobedzki, P., “Photo-Realistic 3D Model Reconstruction,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, USA (May 2006) pp. 3076–3082.Google Scholar
15.Se, S., Lowe, D. and Little, J. J., “Vision-based global localization and mapping for mobile robots,” IEEE Trans. Robot., 21 (3), 364–375 (Jun. 2005).CrossRefGoogle Scholar
16.Davison, A. J., “Real-Time Simultaneous Localization and Mapping with a Single Camera,” Proceedings of the International Conference on Computer Vision and Pattern Recognition, Nice, France (Oct. 2003) pp. 1403–1410.Google Scholar
17.Montiel, J. M. M., Civera, J. and Davison, A. J., “Unified Inverse Depth Parametrization for Monocular SLAM,” Proceedings of the Robotics, Science and Systems, Pennsylvania, USA (Aug. 2006).Google Scholar
18.Bouguet, J.-Y. and Perona, P., “Visual Navigation Using a Single Camera,” Proceedings of the International Conference on Computer Vision, Boston, USA (1995) pp. 645–652.CrossRefGoogle Scholar
19.Dissanayake, M. W. M. 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 (Jun. 2001).CrossRefGoogle Scholar
20.Castellanos, J. A., Neira, J. and Tardos, J. D., “Multisensor fusion for simultaneous localization and map building,” IEEE Trans. Robot. Autom., 17 (6), 908–914 (Dec. 2001).CrossRefGoogle Scholar
21.Ortin, D., Neira, J. and Montiel, J. M. M., “Relocation Using Laser and Vision,” Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 1505–1510.Google Scholar
22.Lowe, D. G., “Object Recognition from Local Scale Invariant Features,” Proceedings of the Seventh International Conference on Computer Vision (ICCV99), Kerkyra, Greece (Sep. 1999) pp. 1150–1157.CrossRefGoogle Scholar
23.Se, S., Lowe, D. and Little, J., “Vision-Based Mobile Robot Localization and Mapping Using Scale Invariant Features,” Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea (May 2001) pp. 2051–2058.Google Scholar
24.Brown, M. and Lowe, D., “Automatic panoramic image stitching using invariant features,” Int. J. Comput. Vis., 74 (1), 59–73 (Aug. 2007).CrossRefGoogle Scholar
25.Clemente, L. A., Davison, A. J., Reid, I. D., Neira, J. and Tardos, J. D., “Mapping Large Loops with a Single Hand-Held Camera,” Proceedings of the Robotics Science and Systems, Atlanta, USA (2007).Google Scholar
26.Davison, A. J., Reid, I. D., Molton, N. D and Stasse, O., “Monoslam: Real-time single camera SLAM,” IEEE Trans. Pattern Anal. Mach. Intell., 29 (6), 1052–1067 (2007).CrossRefGoogle ScholarPubMed
27.Martinez-Cantin, R., Castellanos, J. A., Tardos, J. D. and Montiel, J. M. M., “Adaptive Scale Robust Segmentation for 2d Laser Scanner,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 2006) pp. 796–801.Google Scholar
28.Hutchinson, S., Hager, G. D. and Corke, P. I., “A tutorial on visual servo control,” IEEE Trans. Robot. Autom., 12 (5), 651–670 (1996).CrossRefGoogle Scholar
29.Schulz, D., Burgard, W., Fox, D. and Cremers, A. B., “People tracking with mobile robots using sample-based joint probabilistic data association filters,” Int. J. Robot. Res., 22 (2), 99–116 (Feb. 2003).CrossRefGoogle Scholar
30.Shi, J. and Tomasi, C., “Good features to track,” IEEE Conf. Comput. Vis. Pattern Recognit. (Jun. 1994) pp. 593–600.Google Scholar
31.Frese, U., “Treemap: An o(log(n)) algorithm for indoor simultaneous localization and mapping,” Auton. Robots, 21 (2), 103–122 (Sep. 2006).CrossRefGoogle Scholar