An Improved FastSLAM Framework Based on Particle Swarm Optimization and Unscented Particle Filter ⋆
نویسندگان
چکیده
FastSLAM is a framework which solves the problem of simultaneous localization and mapping using a Rao-Blackwellized particle filter. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling phase. To solve this problem, a FastSLAM method based on particle swarm optimization and unscented particle filter is proposed. The number of particles is seriously reduced because of the particle filter based on the particle swarm optimization for pose estimation; and the landmarks updated through Unscented Kalman filter to improve map estimation accuracy. The method can enhance the SLAM precision effectively, and reduce the particle number and the computational time complexity. The simulation experiment results prove its effectiveness and feasibility.
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