Abstract
In this paper, we investigate the role of iteration in Kalman filters family for improvement of the estimation accuracy of states in simultaneous localization and mapping (SLAM). The linearized error propagation existing in Kalman filters family can result in large errors and inconsistency in the SLAM problem. One approach to alleviate this situation is the use of iteration in extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) based SLAM. The main contribution is to present that the iterated versions of Kalman filters can increase consistency and robustness of these filters against linear error propagation. Experimental results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear observation model in EKF-SLAM and SPKF-SLAM algorithms.
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References
Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: Multaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)
Sibley, G., Sukhatme, G., Matthies, L.: The iterated sigma point Kalman filter with applications to long range stereo. In: Proceeding of Robotics: Science and Systems. Philadelphia, USA (2006)
Andrade-Cetto, J., Vidal-Calleja, T., Sanfeliu, A.: Unscented transformation of vehicle states in SLAM. In: Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation, pp. 324–329. Barcelona, Spain (2005)
Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In: The Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management II. SPIE (1997)
van der Merwe, R.: Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. PhD thesis, OGI School of Science & Engineering, Oregon Health & Science University (2004)
Wan, E.A., van der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Symposium 2000 on Adaptive Systems for Signal Processing (2000)
Martinez-Cantin, R., Castellanos, J.A.: Unscented SLAM for large-scale outdoor environments. In: Proc. of the 2005 IEEE Int. Conf. on Intelligent Robots and Systems, pp. 3427–3432 (2005)
van der Merwe, R., Wan, E., Julier, S.J.: Sigma-point Kalman filters for nonlinear estimation and sensor-fusion: applications to integrated navigation. In: Proceedings of the AIAA Guidance, Navigation & Control Conference. Providence, RI (2004)
Julier, S., Uhlmann, J.K.: A counter example to the theory of simultaneous localization and map building. In: Proc. 2000 IEEE Int. Conf. on Robotics and Automation, pp. 4238–4243. Seoul, Korea (2001)
Negenborn, R.: Robot localization and Kalman filters on finding your position in a noisy world. M.S thesis, Utrecht University (2003)
Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Autonomous Robot Vehicles. Springer (1990)
Bailey, T.: Mobile robot localization and mapping in extensive outdoor environments. PhD thesis, University of Sydney, Australian Centre for Field Robotics (2002)
Frese, U.: An O(log n) algorithm for simultaneous localization and mapping of mobile robots in indoor environments. Ph.D. thesis, University of Erlangen-Nurnberg (2004)
Bar-Shalom, Y., Li, X.-R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. John Wiley and Sons Inc. (2001)
Williams, S.B.: Efficient solution to autonomous mapping and navigation problems. PhD thesis, University of Sydney, Australian Centre for Field Robotics (2001)
Bailey, T., Nieto, J., Guivant, J., Stevens, M., Nebot, E.: Consistency of the EKF-SLAM algorithm. In: IEEE/RSJ International Conference on Robotics and Automation (2006)
Castellanos, J.A., Neira, J., Tardos, J.D.: Limits to the consistency of EKF-based SLAM. In: IFAC Symposium on Intelligent Autonomous Veshicles (2004)
Xavier, J., Pacheco, M., Castro, D., Ruano, A., Nunes, U.: Fast line arc/circle and leg detection from laser scan data in a player driver. In: IEEE International Conference on Robotics and Automation. Barcelona (2005)
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)
Shojaei, Kh., Shahri, A.M.: Iterated unscented SLAM algorithm for navigation of an autonomous mobile robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 22–26 September, Nice, France (2008)
Zhou, W., Zhao, Ch., Guo, J.: The study of improving Kalman filters family for nonlinear SLAM. J. Intell. Robot. Syst. 56(5), 543–564 (2009)
Guivant, J.: Efficient simultaneous localization and mapping in large environments. PhD thesis, University of Sydney, Australian Centre for Field Robotics (2002)
Arras, K.O.: Feature-based robot navigation in known and unknown environments. PhD thesis, EPFL, Lausanne (2003)
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Shojaei, K., Mohammad Shahri, A. Experimental Study of Iterated Kalman Filters for Simultaneous Localization and Mapping of Autonomous Mobile Robots. J Intell Robot Syst 63, 575–594 (2011). https://doi.org/10.1007/s10846-010-9495-7
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DOI: https://doi.org/10.1007/s10846-010-9495-7