Skip to main content
Log in

Monocular Vision for Mobile Robot Localization and Autonomous Navigation

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected during two years in various environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Araújo, H., Carceroni, R.J., and Brown, C.M. 1998. A fully projective formulation to improve the accuracy of Lowe’s pose estimation algorithm. Computer Vision and Image Understanding, 70(2):227–238.

    Article  Google Scholar 

  • Argyros, A., Bekris, K., Orphanoudakis, S., and Kavraki, L. 2005. Robot homing by exploiting panoramic vision. Journal of Autonomous Robots, 19(1):7–25.

    Google Scholar 

  • Blanc, G., Mezouar, Y., and Martinet, P. 2005. Indoor navigation of a wheeled mobile robot along visual routes. In IEEE International Conference on Robotics and Automation, ICRA’05, Barcelonne, Espagne.

  • Cobzas, D., Zhang, H., and Jagersand, M. 2003. Image-based localization with depth-enhanced image map. In International Conference on Robotics and Automation.

  • Davison, A.J. 2003. Real-time simultaneous localisation and mapping with a single camera. In Proceedings of the 9th International Conference on Computer Vision, Nice.

  • de Wit, C.C., Siciliano, B., and Bastin, G. 1996. The Zodiac, Theory of Robot Control. Springer Verlag.

  • Faugeras, O. and Herbert, M. 1986. The representation, recognition, and locating of 3-d objects. International Journal of Robotic Research, 5(3):27–52.

    Article  Google Scholar 

  • Fischler, O. and Bolles, R. 1981. Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communications of the Association for Computing Machinery, 24:381–395.

    Google Scholar 

  • Georgiev, A. and Allen, P.K. 2004. Localization methods for a mobile robot in urban environments. IEEE Transactions on Robotics, 20(5):851–864.

    Article  Google Scholar 

  • Goedemé, T., Tuytelaars, T., Van Gool, L., Vanacker, G., and Nuttin, M. 2005. Feature based omnidirectional sparse visual following. In International Conference on Intelligent Robots and Systems, pp. 1003–1008.

  • Haralick, R., Lee, C., Ottenberg, K., and Nolle, M. 1994. Review and analysis of solutions of the three point perspective pose estimation problem. International Journal of Computer Vision, 13(3):331–356.

    Article  Google Scholar 

  • Harris, C. and Stephens, M. 1988. A combined corner and edge detector. In Alvey Vision Conference, pp. 147–151.

  • Hartley, R. and Zisserman, A. 2000. Multiple View Geometry in Computer Vision. Cambridge University Press.

  • Kidono, K., Miura, J., and Shirai, Y. 2002. Autonomous visual navigation of a mobile robot using a human-guided experience. Robotics and Autonomous Systems, 40(2–3):124–1332.

    Google Scholar 

  • Lavest, J.M., Viala, M., and Dhome, M. 1998. Do we need an accurate calibration pattern to achieve a reliable camera calibration ? In European Conference on Computer Vision, pp. 158–174.

  • Lhuillier, M. and Perriollat, M. 2006. Uncertainty ellipsoids calculations for complex 3d reconstructions. In International Conference on Robotic and Automation.

  • Matsumoto, Y., Inaba, M., and Inoue, H. 1996. Visual navigation using view-sequenced route representation. In International Conference on Robotics and Automation, pp. 83–88.

  • Nistér, D. 2001. Frame decimation for structure and motion. In 2nd Workshop on Structure from Multiple Images of Large Environments, Springer Lecture Notes on Computer Science, vol. 2018, pp. 17–34.

  • Nistér, D. 2003. An efficient solution to the five-point relative pose problem. In Conference on Computer Vision and Pattern Recognition, pp. 147–151.

  • Nistér, D., Naroditsky, O., and Bergen, J. 2004. Visual odometry. In Conference on Computer Vision and Pattern Recognition, pp. 652–659.

  • Remazeilles, A., Chaumette, F., and Gros, P. 2004. Robot motion control from a visual memory. In International Conference on Robotics and Automation, vol. 4, pp. 4695–4700.

  • Royer, E., Bom, J., Dhome, M., Thuilot, B., Lhuillier, M., and Marmoiton, F. 2005a. Outdoor autonomous navigation using monocular vision. In International Conference on Intelligent Robots and Systems, pp. 3395–3400.

  • Royer, E., Lhuillier, M., Dhome, M., and Chateau, T. 2005b. Localization in urban environments: Monocular vision compared to a differential gps sensor. In International Conference on Computer Vision and Pattern Recognition, CVPR.

  • Royer, E., Lhuillier, M., Dhome, M., and Lavest, J.-M. 2005c. Performance evaluation of a localization system relying on monocular vision and natural landmarks. In Proceedings of the ISPRS Workshop BenCOS (Towards Benchmarking Automated Calibration, Orientation and Surface Reconstruction from Images).

  • Samson, C. 1995. Control of chained systems. application to path following and time-varying point stabilization of mobile robots. IEEE Transactions on Automatic Control, 40.

  • Se, S., Lowe, D., and Little, J. 2002. Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotic Research, 21(8):735–760.

    Article  Google Scholar 

  • Simond, N. and Rives, P. 2004. Trajectography of an uncalibrated stereo rig in urban environments. In International Conference on Intelligent Robot and System, pp. 3381–3386.

  • Thuilot, B., Bom, J., Marmoiton, F., and Martinet, P. 2004. Accurate automatic guidance of an urban vehicle relying on a kinematic gps sensor. In Symposium on Intelligent Autonomous Vehicles IAV04.

  • Torr, P., Fitzgibbon, A., and Zisserman, A. 1999. The problem of degeneracy in structure and motion recovery from uncalibrated image sequences. International Journal of Computer Vision, 32(1):27–44.

    Article  Google Scholar 

  • Triggs, B., McLauchlan, P., Hartley, R., and Fitzgibbon, A. 2000. Bundle adjustment—A modern synthesis. In Vision Algorithms: Theory and Practice, W. Triggs, A. Zisserman, and R. Szeliski (Eds.), Lecture Notes in Computer Science, pp. 298–375. Springer Verlag.

  • Vacchetti, L., Lepetit, V., and Fua, P. 2003. Stable 3-d tracking in real-time using integrated context information. In Conference on Computer Vision and Pattern Recognition, Madison, WI.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eric Royer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Royer, E., Lhuillier, M., Dhome, M. et al. Monocular Vision for Mobile Robot Localization and Autonomous Navigation. Int J Comput Vision 74, 237–260 (2007). https://doi.org/10.1007/s11263-006-0023-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-006-0023-y

Keywords

Navigation