Summary
Designing filters exploiting the sparseness of the information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the most recent algorithms that the authors have implemented, Exactly Sparse Extended Information Filter (ESEIF) by Walter et al. [5] and the D-SLAM by Wang et al. [6] are discussed and analyzed in detail. It is proposed that this analysis can stimulate developing a framework suitable for evaluating the relative merits of SLAM algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Frese, U., Larsson, P., Duckett, T.: A multilevel relaxation algorithm for simultaneous localization and mapping. IEEE Transactions on Robotics 21(2), 196–207 (2005)
Thrun, S., Liu, Y., Koller, D., Ng, A.Y., Ghahramani, Z., Durrant-Whyte, H.: Simultaneous localization and mapping with sparse extended information filters. International Journal of Robotics Research 23(7-8), 693–716 (2004)
Dellaert, F., Kaess, M.: Square Root SAM: simultaneous location and mapping via square root information smoothing. International Journal of Robotics Research 25(12), 1181–1203 (2006)
Eustice, R.M., Singh, H., Leonard, J.: Exactly sparse delayed-state filters for view-based SLAM. IEEE Transactions on Robotics 22(6), 1100–1114 (2006)
Walter, M., Eustice, R., Leonard, J.: Exactly sparse Extended Information filters for feature-based SLAM. International Journal of Robotics Research 26(4), 335–359 (2007)
Wang, Z., Huang, S., Dissanayake, G.: D-SLAM: a decoupled solution to simultaneous localization and mapping. International Journal of Robotics Research 26(2), 187–204 (2007)
Kaess, M., Ranganathan, A., Dellaert, F.: Fast incremental square root information smoothing. In: Proc.International Joint Conferences on Artificial Intelligence (IJCAI), pp. 2129–2134 (2007)
Krauthausen, P., Kipp, A., Dellaert, F.: Exploiting locality in SLAM by Nested Dissection. In: Proc. Robotics: Science and Systems (2006)
Paskin, M.: Thin junction tree filters for simultaneous localization and mapping. In: Proc.International Joint Conferences on Artificial Intelligence (IJCAI), pp. 1157–1164 (2003)
Folkesson, J., Christensen, H.I.: Graphical SLAM - a self-correcting map. In: Proc. IEEE International Conference on Robotics and Automation, pp. 383–390 (2004)
Thrun, S., Montemerlo, M.: The GraphSLAM algorithm with applications to large-scale mapping of urban structures. International Journal of Robotics Research 25(5-6), 403–429 (2004)
Eustice, R., Singh, H., Leonard, J., Walter, M., Ballard, R.: Visually navigating the RMS Titanic with SLAM information filters. In: Proc. Robotics: Science and Systems (2005)
Frese, U.: A proof for the approximate sparsity of SLAM information matrices. In: Proc. IEEE International Conference on Robotics and Automation, pp. 331–337 (2005)
Guivant, J.E., Nebot, E.M.: Optimization of the simultaneous localization and map building (SLAM) algorithm for real time implementation. IEEE Transactions on Robotics and Automation 17(3), 242–257 (2001)
Huang, S., Wang, Z., Dissanayake, G.: Mapping large scale environments using relative position information among landmarks. In: Proc. International Conference on Robotics and Automation, pp. 2297–2302 (2006)
Wang, Z.: Exactly sparse information filters for simultaneous localization and mapping. Ph.D. Thesis. Centre of Excellence for Autonomous Systems, University of Techology, Sydney (2007)
Ni, K., Steedly, D., Dellaert, F.: Tectonic SAM: exact, out-of-core, submap-based SLAM. In: Proc. IEEE International Conference on Robotics and Automation, pp. 1678–1685 (2007)
Frese, U.: Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping. Autonomus Robots 21, 103–122 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Wang, Z., Huang, S., Dissanayake, G. (2008). Tradeoffs in SLAM with Sparse Information Filters. In: Laugier, C., Siegwart, R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75404-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-75404-6_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75403-9
Online ISBN: 978-3-540-75404-6
eBook Packages: EngineeringEngineering (R0)