This paper presents HybridSLAM: an approach to SLAM which combines the strengths and avoids the weaknesses of two popular mapping strategies: FastSLAM and EKF-SLAM. FastSLAM is used as a front-end, producing local maps which are periodically fused into an EKF-SLAM back-end. The use of FastSLAM locally avoids linearisation of the vehicle model and provides a high level of robustness to clutter and ambiguous data association. The use of EKFSLAM globally allows uncertainty to be remembered over long vehicle trajectories, avoiding FastSLAM's tendency to become over-confident. Extensive trials in randomly-generated simulated environments show that HybridSLAM significantly out-performs either pure approach. The advantages of HybridSLAM are most pronounced in cluttered environments where either pure approach encounters serious difficulty. In addition, the HybridSLAM algorithm is experimentally validated in a real urban environment. © 2009 Springer-Verlag.
CITATION STYLE
Brooks, A., & Bailey, T. (2010). HybridSLAM: Combining FastSLAM and EKF-SLAM for reliable mapping. In Springer Tracts in Advanced Robotics (Vol. 57, pp. 647–661). https://doi.org/10.1007/978-3-642-00312-7_40
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