Single and Multi Camera Simultaneous Localization and Mapping Using the Extended Kalman Filter

On the Different Parameterizations for 3D Point Features
  • Simone Ceriani
  • Daniele Marzorati
  • Matteo Matteucci
  • Domenico G. Sorrenti


Simultaneous Localization and Mapping (SLAM) has received quite a lot of attention in the last decades because of its relevance for many applications centered on a mobile observer, such as service robotics and intelligent transportation systems. This paper focuses on the use of recursive Bayesian filtering, as implemented by the Extendend Kalman Filter (EKF), to face the Visual SLAM problem, i.e., when using data from visual sources. In Monocular SLAM, which uses a single camera as unique source of information, it is not possible to directly estimate the depth of a feature from a single image. To handle the severely non-normal distribution representing such uncertainty, inverse parametrizations were developed, capable to deal with such uncertainty and still relying on Gaussian variables. In the paper, after an introduction to EKF-SLAM, we provide a review of different inverse parametrizations, and we introduce a novel proposal, the Framed Inverse Depth (FID) parametrization, which, in terms of consistency, performs similarly to state of the art Monocular SLAM parametrizations, but at a reduced computational cost. All these parametrizations can be used in a stereo and multi camera setting too. An extensive analysis is presented for both Monocular and stereo SLAM, for a simulated environment widely used in the literature as well as on a widely used real dataset.


Simultaneous localization and mapping Extended Kalman filter Computer vision Robotics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, M., Konolige, K.: Frameslam: From bundle adjustment to real-time visual mapping. IEEE Trans. Robot. 24(5), 1066–1077 (2008)Google Scholar
  2. 2.
    Andrade-Cetto, J., Vidal-Calleja, T., Sanfeliu, A.: Unscented transformation of vehicle states in slam. In: Robotics and Automation, 2005. ICRA 2005. In: IEEE International Conference on Proceedings of the 2005, pp. 323–328 (2005). doi: 10.1109/ROBOT.2005.1570139
  3. 3.
    Bailey, T., Nieto, J., Guivant, J., Stevens, M., Nebot, E.: Consistency of the ekf-slam algorithm. In: Proc. of IEEE Intern. Conf. on Intelligent Robots and Systems, pp. 3562–3568 (2006)Google Scholar
  4. 4.
    Barfoot, T.: Online visual motion estimation using fastslam with sift features. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005), pp. 579–585 (2005). doi: 10.1109/IROS.2005.1545444
  5. 5.
    Castellanos, J.A., Martinez-Cantin, R., Tardos, J.D., Neira, J.: Robocentric map joining: improving the consistency of EKF-SLAM. Robot. Auton. Syst. 55(1), 21–29 (2007)CrossRefGoogle Scholar
  6. 6.
    Ceriani, S., Fontana, G., Giusti, A., Marzorati, D., Matteucci, M., Migliore, D., Rizzi, D., Sorrenti, D.G., Taddei, P.: Rawseeds ground truth collection systems for indoor self-localization and mapping. Auton. Robots 27(4), 353–371 (2009)CrossRefGoogle Scholar
  7. 7.
    Ceriani, S., Marzorati, D., Matteucci, M., Migliore, D., Sorrenti, D.G.: On feature parameterization for ekf-based monocular slam. In: proceedings of 18th World Congress of the International Federation of Automatic Control (IFAC), pp. 6829–6834 (2011)Google Scholar
  8. 8.
    Chekhlov, D., Pupilli, M., Mayol-Cuevas, W., Calway, A.: Real-time and robust monocular slam using predictive multi-resolution descriptors. In: 2nd International Symposium on Visual Computing. URL (2006)
  9. 9.
    Civera, J., Davison, A.J., Montiel, J.M.M.: Inverse depth to depth conversion for monocular slam. In: Proc. of IEEE Intern. Conf. on Robotics and Automation, pp. 2778–2783 (2007)Google Scholar
  10. 10.
    Civera, J., Grasa, O., Davison., A., Montiel, J.M.M.: 1-point RANSAC for Extended Kalman filtering: Application to real-time structure from motion and visual odometry. JFR 27, 609–631 (2010)Google Scholar
  11. 11.
    Davison, A.: Real-time simultaneous localisation and mapping with a single camera. In: Proc. of IEEE Intern. Conf. on Computer Vision (2003)Google Scholar
  12. 12.
    Davison, A.J., Reid, I.D., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  13. 13.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part i. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006). doi: 10.1109/MRA.2006.1638022 CrossRefGoogle Scholar
  14. 14.
    Funda, J., Paul, R.: A comparison of transforms and quaternions in robotics. In: Proc. of the 1988 IEEE Intern. Conf. on Robotics and Automation, vol. 2, pp. 886–891 (1988)Google Scholar
  15. 15.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)Google Scholar
  16. 16.
    Holmes, S., Klein, G., Murray, D.: An o(n 2) square root unscented kalman filter for visual simultaneous localization and mapping. IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 1251–1263 (2009). doi: 10.1109/TPAMI.2008.189 CrossRefGoogle Scholar
  17. 17.
    Imre, E., Berger, M., Noury, N.: Improved inverse-depth parameterization for monocular simultaneous localization and mapping. In: Proc. of IEEE Intern. Conf. on Robotics and Automation, pp. 381–386 (2009)Google Scholar
  18. 18.
    Klein, G., Murray, D.: Parallel tracking and mapping on a camera phone. In: Proc. Eigth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’09). Orlando (2009)Google Scholar
  19. 19.
    Lin, K.H., Wang, C.C.: Stereo-based simultaneous localization, mapping and moving object tracking. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Taipei, Taiwan (2010)Google Scholar
  20. 20.
    Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Auton. Robots 4, 333–349 (1997)CrossRefGoogle Scholar
  21. 21.
    Martinez-Cantin, R., Castellanos, J.: Bounding uncertainty in EKF-SLAM: The robocentric local approach. In: Proc. of IEEE Intern. Conf. on Robotics and Automation (2006)Google Scholar
  22. 22.
    Marzorati, D., Matteucci, M., Migliore, D., Sorrenti, D.G.: On the use of inverse scaling in monocular slam. In: Proc. of IEEE Intern. Conf. on Robotics and Automation, pp. 2030–2036 (2009)Google Scholar
  23. 23.
    Migliore, D., Rigamonti, R., Marzorati, D., Matteucci, M., Sorrenti, D.G.: Use a single camera for simultaneous localization and mapping with mobile object tracking in dynamic environments. In: Proceedings of International Workshop on Safe Navigation in Open and Dynamic Environments Application to Autonomous Vehicles (2009)Google Scholar
  24. 24.
    Montiel, J., Civera, J., Davison, A.J.: Unified inverse depth parametrization for monocular slam. In: Proc. of Robotics: Science and Systems (2006)Google Scholar
  25. 25.
    Newcombe, R., Davison, A.: Live dense reconstruction with a single moving camera. In: 2010 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1498–1505 (2010)Google Scholar
  26. 26.
    Pietzsch, T.: Efficient feature parameterisation for visual slam using inverse depth bundles. In: Proc. of BMVC Conf. (2008)Google Scholar
  27. 27.
    Piniés, P., Paz, L.M., Gálvez-López, D., Tardós, J.D.: CI-Graph simultaneous localization and mapping for three-dimensional reconstruction of large and complex environments using a multicamera system. JFR 27(5), 561–586 (2010)Google Scholar
  28. 28.
    Pinies, P., Tardos, J.: Large-scale slam building conditionally independent local maps: application to monocular vision. IEEE Trans. Robot. 24(5), 1094–1106 (2008). doi: 10.1109/TRO.2008.2004636 CrossRefGoogle Scholar
  29. 29.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: European Conference on Computer Vision, vol. 1, pp. 430–443 (2006). doi: 10.1007/11744023_34
  30. 30.
    Sim, R., Elinas, P., Little, J.: A study of the rao-blackwellised particle filter for efficient and accurate vision-based slam. Int. J. Comput. Vis. 74, 303–318 (2007). doi: 10.1007/s11263-006-0021-0 CrossRefGoogle Scholar
  31. 31.
    Solà, J.: Consistency of the monocular EKF-SLAM algorithm for three different landmark parametrizations. In: 2010 IEEE Intern. Conf. on Robotics and Automation (ICRA), pp. 3513–3518. IEEE (2010)Google Scholar
  32. 32.
    Solà, J., Monin, A., Devy, M.: Bicamslam: two times mono is more than stereo. In: 2007 IEEE Intern. Conf. on Robotics and Automation (ICRA), pp. 4795–4800 (2007)Google Scholar
  33. 33.
    Solà, J., Monin, A., Devy, M., Lemaire, T.: Undelayed initialization in bearing only slam. In: Proc. of Intern. Conf. on Intelligent Robots and Systems, pp. 2499–2504 (2005)Google Scholar
  34. 34.
    Solà, J., Vidal-Calleja, T., Civera, J., Montiel, J.M.M.: Impact of landmark parametrization on monocular EKF-SLAM with points and lines. Int. J. Comput. Vis. Available online at Springer’s: (2011)
  35. 35.
    Strasdat, H., Montiel, J., Davison, A.: Real-time monocular slam: why filter? In: Proceedings of IEEE International Conference on and Automation (ICRA), pp. 2657–2664 (2010)Google Scholar
  36. 36.
    Strasdat, H., Montiel, J.M.M., Davison, A.: Scale drift-aware large scale monocular slam. In: Proceedings of Robotics: Science and Systems. Zaragoza, Spain (2010)Google Scholar
  37. 37.
    Thrun, S., Montemerlo, M.: The GraphSLAM algorithm with applications to large-scale mapping of urban structures. IJRR 25(5/6), 403–430 (2005)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Simone Ceriani
    • 1
  • Daniele Marzorati
    • 2
  • Matteo Matteucci
    • 1
  • Domenico G. Sorrenti
    • 3
  1. 1.Politecnico di Milano (DEIB)MilanoItaly
  2. 2.InfoSolution S.p.A.Vimodrone (Mi)Italy
  3. 3.Università degli Studi di Milano - Bicocca (DISCo)MilanoItaly

Personalised recommendations