Abstract
In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [12]. Various filtering methods – Particle Filtering, and derived Kalman Filter methods (Extended, Unscented) – have been applied successfully to SLAM. We present a new algorithm that adapts the Square Root Unscented Transformation [13], previously only applied to feature based maps [5], to grid mapping. We also present a new method for the so-called pose-correction step in the algorithm. Experimental results show improved computational performance on more complex grid maps compared to an existing grid based particle filtering algorithm.
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References
Bailey, T., Nieto, J., Guivant, J., Stevens, M., Nebot, E.: Consistency of the EKF-SLAM algorithm. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3562–3568 (2006)
Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based slam with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 2432–2437 (2005)
Grisetti, G., Tipaldi, G., Stachniss, C., Burgard, W.: GMapping Algorithm, http://www.openslam.org/
Higham, N.: Analysis of the Cholesky decomposition of a semi-definite matrix. Reliable Numerical Computation (1990)
Holmes, S., Klein, G., Murray, D.: A Square Root Unscented Kalman Filter for visual monoSLAM. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 3710–3716 (2008)
Howard, A., Roy, N.: The Robotics Data Set Repository (radish), http://radish.sourceforge.net/
Kim, C., Sakthivel, R., Chung, W.: Unscented FastSLAM: A robust algorithm for the simultaneous localization and mapping problem. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 2439–2445 (2007)
Martinez-Cantin, R., Castellanos, J.: Unscented SLAM for large-scale outdoor environments. In: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Citeseer, pp. 328–333 (2005)
Montemerlo, M., CARMEN-team: CARMEN: The Carnegie Mellon Robot Navigation Toolkit 2002, http://carmen.sourceforge.net
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: Proc. of the National Conf. on Artificial Intelligence, pp. 593–598. AAAI Press/MIT Press, Menlo Park, Cambridge (1999/2002)
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: Proc. of Int. Joint Conf. on Artificial Intelligence, vol. 18, pp. 1151–1156 (2003)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Van der Merwe, R., Doucet, A., De Freitas, N., Wan, E.: The unscented particle filter. In: Adv. in Neural Information Processing Systems, pp. 584–590 (2001)
Van Der Merwe, R., Wan, E.: The square-root unscented Kalman filter for state and parameter-estimation. In: Proc. of IEEE Int. Conf. on Acoustics Speech and Signal Processing, vol. 6, pp. 3461–3464 (2001)
Wan, E., Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Proc. of the IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium, pp. 153–158 (2000)
Zandara, S., Nicholson, A.: Square Root Unscented Particle Filtering for Grid Mapping. Technical report 2009/246, Clayton School of IT, Monash University (2009)
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Zandara, S., Nicholson, A. (2009). Square Root Unscented Particle Filtering for Grid Mapping. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_13
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DOI: https://doi.org/10.1007/978-3-642-10439-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
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