| Citation: | LUO Jingwen, QIN Shiyin. FastSLAM for mobile robot based on box particle filter with intelligence optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 53-66. doi: 10.13700/j.bh.1001-5965.2020.0549(in Chinese)
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The traditional FastSLAM algorithm requires a large number of particles to build the map, thus resulting in high computational complexity and difficulty in improving the estimation accuracy. In view of these problems, an algorithm of FastSLAM for mobile robot is presented based on box particle filter with intelligence optimization (IOBPF). First, the dynamic optimization mechanism of firefly algorithm (FA) is applied to the box particle filter (BPF), and the formulas of fluorescence brightness updating, attraction calculation and position updating of box particle are constructed, which makes the box particles move toward the high-likelihood region intelligently and avoid the phenomenon of box particle degeneracy. Then, the improved BPF with intelligence optimization is utilized to estimate the pose of robot, and the extended interval Kalman filter (EIKF) is employed to complete the map building and updating. The results of model simulation and entity experiment of mobile robot show that the intelligent FastSLAM algorithm in this paper can effectively improve the performance of box particles and reduce the number of particles required for map construction, thus significantly improving the positioning accuracy and robustness of map construction.
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