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SLAM algorithm with Gaussian distributed resampling Rao-Blackwellized particle filter. (Chinese. English summary) Zbl 1389.93230

Summary: For the estimation problem that the Rao-Blackwellized Particle Filter (RBPF)-SLAM algorithm used in mobile robots suffers from sample impoverishment in grid mapping, a Gaussian Distributed Resampling( GDR) based RBPF-SLAM algorithm is proposed. Firstly, the improved algorithm sorts particles according to the weight size. Furthermore, Gaussian distributed resampling is applied to disperse the high-weight particles so as to generate new particles. By using GDR, particle diversity can be maintained and sample impoverishment can be avoided. Thus accurate grid mapping is guaranteed. Experimental results show the effectiveness of the proposed algorithm. Meanwhile, the results prove that the proposed algorithm guarantees reliable estimation with less samples, and the computation burden can be reduced efficiently.

MSC:

93E11 Filtering in stochastic control theory
68T40 Artificial intelligence for robotics
93C85 Automated systems (robots, etc.) in control theory
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