Abstract
In this paper, a new Simultaneous Localization and Mapping (SLAM) method is proposed, called L-SLAM, which is a Low dimension version of the FastSLAM family algorithms. L-SLAM uses a particle filter of lower dimensionality than FastSLAM and achieves better accuracy than FastSLAM 1.0 and 2.0 for a small number of particles. L-SLAM is suitable for high dimensionality problems which exhibit high computational complexity like 6-dof 3D SLAM. Unlike FastSLAM algorithms which use Extended Kalman Filters (EKF), the L-SLAM algorithm updates the particles using linear Kalman filters. A planar SLAM problem of a rear drive car-like robot as well as a three dimensional SLAM problem with 6-dof of an airplane robot is presented. Experimental results based on real case scenarios using the Car Park and Victoria Park datasets are presented for the planar SLAM. Also results based on simulated environment and real case scenario of the Koblenz datasets are presented and compared with the three dimensional version of the FastSLAM 1.0 and 2.0. The experimental results demonstrate the superiority of the proposed method over FastSLAM 1.0 and 2.0.
Similar content being viewed by others
References
Augenstein, S., Rock, S.: Simultaneous estimation of target pose and 3-d shape using the fastslam algorithm. In: Proceedings of AIAA GNC 2009 (2009)
Briers, M., Doucet, A., Maskell, S.: Smoothing algorithms for state–space models. Ann. Inst. Stat. Math. 62(1), 61–89 (2010). doi:10.1007/s10463-009-0236-2
Dissanayake, M., Newman, P., Clark, S., Durrant-Whyte, H., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)
Doucet, A., de Freitas, N., Murphy, K.P., Russell, S.J.: Rao-blackwellised particle filtering for dynamic Bayesian networks. In: UAI, pp. 176–183 (2000)
Kwak, N., Kim, G.W., Lee, B.H.: A new compensation technique based on analysis of resampling process in fastslam. Robotica 26(2), 205–217 (2008). doi:10.1017/S0263574707003773
Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using fastslam. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA ’03, vol. 2, pp. 1985–1991 (2003). doi:10.1109/ROBOT.2003.1241885
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI, pp. 593–598 (2002)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: IJCAI, pp. 1151–1156 (2003)
Montemerlo, M., Thrun, S., Siciliano, B.: FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics, vol. 27. Springer, Berlin (2007)
Mullane, J., Vo, B.N., Adams, M., Vo, B.T.: A random finite set approach to bayesian slam. In: IEEE trans. robotics, vol. 27, pp. 268–282 (2011)
Mullane, J., Vo, B.N., Adams, M., Vo, B.T.: Random Finite Sets for Robotic Mapping and Slam: Tracts in Advanced Robotics. Springer (2011)
Nieto, J., Guivant, J., Nebot, E., Thrun, S.: Real time data association for fastslam. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA ’03, vol. 1, pp. 412–418 (2003)
Nüchter, A.: 3D robotic mapping: the simultaneous localization and mapping problem with six degrees of freedom. In: STAR: Springer tracts in advanced robotics, vol. 52. Springer, (2009). http://www.loc.gov/catdir/enhancements/fy1102/2008941001-d.html
Petridis, V., Zikos, N.: L-slam: reduced dimensionality fastslam algorithms. In: WCCI, pp. 2510–2516, Barcelona (2010)
Rauch, H.E., Striebel, C.T., Tung, F.: Maximum likelihood estimates of linear dynamic systems. J. Am. Inst. Aeronaut. Astronaut. 3(8), 1445–1450 (1965)
Särkkä, S., Vehtari, A., Lampinen, J.: Time series prediction by kalman smoother with cross validated noise density. In: IJCNN, pp. 1653–1658. (2004) doi:10.1109/IJCNN.2004.1380200
Sarkka, S., Vehtari, A., Lampinen, J.: Cats benchmark time series prediction by kalman smoother with cross- validated noise density. Neurocomputer 70(13–15), 2331–2341 (2007). doi:10.1016/j.neucom.2005.12.132
Sasiadek, J., Monjazeb, A., Necsulescu, D.: Navigation of an autonomous mobile robot using ekf-slam and fastslam. In: 2008 16th Mediterranean conference on Control and automation, pp. 517–522. (2008) doi:10.1109/MED.2008.4602213
Smith, R., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Int. J. Robot. Res. 5(4), 56–68 (1986)
Smith, R., Self, M., Cheeseman, P.: A stochastic map for uncertain spatial relationships. In: Proceedings of the 4th International Symposium on Robotics Research, pp. 467–474. MIT Press, Cambridge (1988)
Thrun, S., Montemerlo, M., Koller, D., Wegbreit, B., Nieto, J., Nebot, E.: Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association. J. Mach. Learn. Res. (2004)
Zikos, N., Petridis, V.: L-slam: reduced dimensionality fastslam with unknown data association. In: ICRA, pp. 4074–4079. Shanghai (2011)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zikos, N., Petridis, V. 6-DoF Low Dimensionality SLAM (L-SLAM). J Intell Robot Syst 79, 55–72 (2015). https://doi.org/10.1007/s10846-014-0029-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-014-0029-6