Summary
A FastSLAM approach to the SLAM problem is considered in this paper. An improvement to the classical FastSLAM algorithm has been obtained by replacing the Extended Kalman Filters used in the prediction step and in the feature update with Unscented Kalman Filters and by introducing an adaptive selective resampling. The simulations confirm the effectiveness of the proposed modifications.
This work has been partially supported by MIUR under grant PRIN 2005092439. The authors would like to thank F. Romanelli and L. Spinello for their valuable initial support.
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Cugliari, M., Martinelli, F. (2008). A FastSLAM Algorithm Based on the Unscented Filtering with Adaptive Selective Resampling. 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_34
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DOI: https://doi.org/10.1007/978-3-540-75404-6_34
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