View-based SLAM using Omnidirectional Images

D. Valiente, A. Gil, L. Fernández, O. Reinoso


In this paper we focus on the problem of Simultaneous Localization and Mapping (SLAM) using visual information obtained from the environment. In particular, we propose the use of a single omnidirectional camera to carry out this task. Many approaches to visual SLAM concentrate on the estimation of the position of a set of 3D points, commonly denoted as visual landmarks which are extracted from images acquired at the environment. Thus the complexity of the map computation grows as the number of visual landmarks in the map increases. In this paper we propose a different representation of the environment that presents a series of advantages compared to the before mentioned approaches, such as a simplified computation of the map and a more compact representation of the environment. Concretely, the map is represented by a set of views captured from particular places in the environment. Each view is composed by its position and orientation in the map and a set of 2D interest points represented in the image reference frame. Thus, in each view the relative orientation of a set of visual landmarks is stored. During the map building stage, the robot captures an image and finds corresponding points between the current view and the views stored in the map. Assuming that a set of corresponding points is found, the transformation between both views can be computed, thus allowing us to build the map and estimate the pose of the robot. In the suggested framework, the problem of finding correspondences between views is troublesome. Consequently, with the aim of performing a more reliable approach, we propose a new method to find correspondences between two omnidirectional images when the relative error between them is modeled by a gaussian distribution which correlates the current error on the map. In order to validate the ideas presented here, we have carried out a series of experiment in a real environment using real data. Experiment results are presented to demonstrate the validity of the proposed solution.


  1. Andrew J. Davison, A. J., Gonzalez Cid, Y., and Kita, N. (2004). Improving data association in vision-based SLAM. In Proc. of IFAC/EURON, Lisboa, Portugal.
  2. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). SURF: Speeded up robust features. In Proc. of the ECCV, Graz, Austria.
  3. Bunschoten, R. and Krose, B. (2003). Visual odometry from an omnidirectional vision system. In Proc. of the ICRA, Taipei, Taiwan.
  4. Civera, J., Davison, A. J., and Martínez Montiel, J. M. (2008). Inverse depth parametrization for monocular slam. IEEE Trans. on Robotics.
  5. Davison, A. J. and Murray, D. W. (2002). Simultaneous localisation and map-building using active vision. IEEE Trans. on PAMI.
  6. Gil, A., Martinez-Mozos, O., Ballesta, M., and Reinoso, O. (2010a). A comparative evaluation of interest point detectors and local descriptors for visual slam. Machine Vision and Applications.
  7. Gil, A., Reinoso, O., Ballesta, M., Juliá, M., and Payá, L. (2010b). Estimation of visual maps with a robot network equipped with vision sensors. Sensors.
  8. Grisetti, G., Stachniss, C., Grzonka, S., and Burgard, W. (2007). A tree parameterization for efficiently computing maximum likelihood maps using gradient descent. In Proc. of RSS, Atlanta, Georgia.
  9. Harris, C. G. and Stephens, M. (1988). A combined corner and edge detector. In Proc. of Alvey Vision Conference, Manchester, UK.
  10. Hartley, R. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press.
  11. Jae-Hean, K. and Myung Jin, C. (2003). Slam with omnidirectional stereo vision sensor. In Proc. of the IROS, Las Vegas (Nevada).
  12. Joly, C. and Rives, P. (2010). Bearing-only SAM using a minimal inverse depth parametrization. In Proc. of ICINCO, Funchal, Madeira (Portugal).
  13. Kawanishi, R., Yamashita, A., and Kaneko, T. (2008). Construction of 3D environment model from an omnidirectional image sequence. In Proc. of the Asia International Symposium on Mechatronics 2008, Sapporo, Japan.
  14. Montemerlo, M., Thrun, S., Koller, D., and Wegbreit, B. (2002). Fastslam: a factored solution to the simultaneous localization and mapping problem. In Proc. of the 18th national conference on Artificial Intelligence, Edmonton, Canada.
  15. Murillo, A. C., Guerrero, J. J., and Sagüés, C. (2007). SURF features for efficient robot localization with omnidirectional images. In Proc. of the ICRA, San Diego, USA.
  16. Nister, D. (2003). An efcient solution to the five-point relative pose problem. In Proc. of the IEEE CVPR, Madison, USA.
  17. Scaramuzza, D. (2011). Performance evaluation of 1-point RANSAC visual odometry. Journal of Field Robotics.
  18. Scaramuzza, D., Fraundorfer, F., and Siegwart, R. (2009). Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC. In Proc. of the ICRA, Kobe, Japan.
  19. Stachniss, C., Grisetti, G., Haehnel, D., and Burgard, W. (2004). Improved Rao-Blackwellized mapping by adaptive sampling and active loop-closure. In Proc. of the SOAVE, Ilmenau, Germany.
  20. Stewenius, H., Engels, C., and Nister, D. (2006). Recent developments on direct relative orientation. ISPRS Journal of Photogrammetry and Remote Sensing.

Paper Citation

in Harvard Style

Valiente D., Gil A., Fernández L. and Reinoso O. (2012). View-based SLAM using Omnidirectional Images . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8565-22-8, pages 48-57. DOI: 10.5220/0004031800480057

in Bibtex Style

author={D. Valiente and A. Gil and L. Fernández and O. Reinoso},
title={View-based SLAM using Omnidirectional Images},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},

in EndNote Style

JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - View-based SLAM using Omnidirectional Images
SN - 978-989-8565-22-8
AU - Valiente D.
AU - Gil A.
AU - Fernández L.
AU - Reinoso O.
PY - 2012
SP - 48
EP - 57
DO - 10.5220/0004031800480057