Autonomous Robots

, Volume 43, Issue 3, pp 727–739 | Cite as

Windowed multiscan optimization using weighted least squares for improving localization accuracy of mobile robots

  • G. V. OvchinnikovEmail author
  • A. L. Pavlov
  • D. Tsetserukou


The localization and trajectory estimation of mobile robots is one of the fundamental problems in contemporary robotics. To solve it, robots often rely on the laser scanner data, which is being processed by scan-matcher algorithms followed by a simple integration of acquired transformations. Here we propose algorithm to improve the accuracy of trajectory estimation using additional correspondences between scans and the idea that all transformations between pairs of “not too far away" scans should be consistent between themselves. Additionally, weighting based on the scan-matcher error estimation allows us to reduce the importance of scan-matcher results, which can not be reliably matched. Our approach can be used to improve the performance of existing simultaneous localization and mapping setups in the form of an easily pluggable middleware, which depends only on the laser scanner and odometry data. Experimental evaluation on MIT Stata Center dataset shows that our method outperforms standard keyframe approach by more than 20% by root mean square error metric. In an experiment performed at the Skoltech using different setup our method showed almost 35% improvement.


Scan matching Localization Optimization Weighted least squares Mobile robot 



The authors are grateful to Dr. Evgeny G. Mironov, Dmitry Mironov and Yuri Sarkisov for discussion and valuable suggestions on the paper content.


  1. Anderson, E., Bai, Z., Bischof, C., Blackford, L. S., Demmel, J., Dongarra, J., et al. (1999). LAPACK users’ guide. Philadelphia: SIAM.CrossRefzbMATHGoogle Scholar
  2. Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping (slam): Part ii. IEEE Robotics & Automation Magazine, 13(3), 108–117.CrossRefGoogle Scholar
  3. Bengtsson, O. (2006). Robust self-localization of mobile robots in dynamic environments using scan-matching algorithms. Gothenburg: Chalmers University of Technology.Google Scholar
  4. Biber, P., & Straßer, W. (2003). The normal distributions transform: A new approach to laser scan matching. In Proceedings of the 2003 IEEE/RSJ international conference on intelligent robots and systems, (IROS 2003) (Vol. 3, pp. 2743–2748). New York: IEEE.Google Scholar
  5. Biber, P., & Strasser, W. (2006). nscan-matching: Simultaneous matching of multiple scans and application to slam. In Proceedings 2006 IEEE international conference on robotics and automation, ICRA 2006 (pp. 2270–2276). New York: IEEE.Google Scholar
  6. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016). Simultaneous localization and mapping: Present, future, and the robust-perception age. arXiv preprint arXiv:1606.05830.
  7. Censi, A. (2007). An accurate closed-form estimate of ICP’s covariance. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 3167–3172). Rome, Italy, AprilGoogle Scholar
  8. Censi, A. (2008). An ICP variant using a point-to-line metric. In Proceedings of the IEEE international conference on robotics and automation (ICRA), Pasadena, CA, May.Google Scholar
  9. Censi, A. (2009). On achievable accuracy for pose tracking. In IEEE international conference on robotics and automation, 2009. ICRA’09 (pp. 1–7). New York: IEEE.Google Scholar
  10. Chen, S. Y. (2012). Kalman filter for robot vision: A survey. IEEE Transactions on Industrial Electronics, 59(11), 4409–4420.CrossRefGoogle Scholar
  11. Christopher, C. P., & Saunders, M. A. (1982). Lsqr: An algorithm for sparse linear equations and sparse least squares. ACM Transactions on Mathematical Software, 8(1), 43–71.MathSciNetCrossRefzbMATHGoogle Scholar
  12. Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: part i. IEEE Robotics and Automation Magazine, 13(2), 99–110.CrossRefGoogle Scholar
  13. Frese, U. (2006). A discussion of simultaneous localization and mapping. Autonomous Robots, 20(1), 25–42.CrossRefGoogle Scholar
  14. Gamage, D., & Drummond, T. (2015). Reduced dimensionality extended Kalman filter for slam in a relative formulation. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1365–1372). New York: IEEE.Google Scholar
  15. Golub, G. H., & Van Loan, C. F. (2012). Matrix computations (Vol. 3). Baltimore: JHU Press.zbMATHGoogle Scholar
  16. Huang, G. P., Mourikis, A. I., & Roumeliotis, S. I. (2013). A quadratic-complexity observability-constrained unscented Kalman filter for slam. IEEE Transactions on Robotics, 29(5), 1226–1243.CrossRefGoogle Scholar
  17. Menegatti, E., Maeda, T., & Ishiguro, H. (2004). Image-based memory for robot navigation using properties of omnidirectional images. Robotics and Autonomous Systems, 47(4), 251–267.CrossRefGoogle Scholar
  18. Nocedal, J., & Wright, S. J. (2006). Numerical Optimization. Berlin: Springer.zbMATHGoogle Scholar
  19. Paige, C. C., & Saunders, M. A. (1982). Algorithm 583: LSQR: Sparse linear equations and least squares problems. ACM Transactions on Mathematical Software (TOMS), 8(2), 195–209.CrossRefGoogle Scholar
  20. Pfister, S. T., Kriechbaum, K. L., Roumeliotis, S. I., & Burdick, J. W. (2002). Weighted range sensor matching algorithms for mobile robot displacement estimation. In Proceedings of the IEEE international conference on robotics and automation, 2002 ICRA’02 (Vol 2, pp. 1667–1674). New YorK: IEEE.Google Scholar
  21. Piniés, P., Paz, L. M., & Tardós, J. D. (2009). Ci-graph: An efficient approach for large scale slam. In ICRA’09. IEEE international conference on robotics and automation, 2009 (pp 3913–3920). New York: IEEE.Google Scholar
  22. Röwekämper, J., Sprunk, C., Tipaldi, G. D., Stachniss, C., Pfaff, P., & Burgard, W. (2012). On the position accuracy of mobile robot localization based on particle filters combined with scan matching. In 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3158–3164). New York: IEEE.Google Scholar
  23. Sugiyama, J., Tsetserukou, D., & Miura, J. (2011). Navigoid: Robot navigation with haptic vision. In SIGGRAPH Asia 2011 emerging technologies (pp. 9). New York: ACM.Google Scholar
  24. Thrun, S., & Montemerlo, M. (2006). The graph slam algorithm with applications to large-scale mapping of urban structures. The International Journal of Robotics Research, 25(5–6), 403–429.CrossRefGoogle Scholar
  25. Tsetserukou, D., Sugiyama, J., & Miura, J. (2011). Belt tactile interface for communication with mobile robot allowing intelligent obstacle detection. In World Haptics conference (WHC), 2011 IEEE (pp. 113–118). New York: IEEE.Google Scholar
  26. Zhang, J., Kaess, M., & Singh, S. (2017). A real-time method for depth enhanced visual odometry. Autonomous Robots, 41(1), 31–43.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • G. V. Ovchinnikov
    • 1
    Email author
  • A. L. Pavlov
    • 2
  • D. Tsetserukou
    • 2
  1. 1.Skolkovo Institute of Science and Technology Center for Computational Data-Intensive Science and EngineeringRussian Federation, Skolkovo Innovation CenterMoscowRussia
  2. 2.Skolkovo Institute of Science and TechnologySpace CenterSkolkovoRussia

Personalised recommendations