| Author Name | Affiliation | | Weinan Chen | School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China | | Yisheng Guan | School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China | | Hong Zhang | School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China | | Lei Zhu | School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China |
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| Abstract: |
| RBPF (Rao-Blackwellized Particle Filter) is a popular PF (Particle Filter) in decreasing the dimension of estimation problems and FastSLAM (Fast Simultaneous Localization and Mapping) is a RBPF-based algorithm. In FastSLAM, each particle carries a large amount of data which results in low computing efficiency and large memory space occupancy. To solve this problem, a RBPF algorithm with non-intact particle data is studied. The key idea is to differentiate the particle data. Through the screening of particles, the number of particles carrying individual map data is limited to reduce the data occupied space and speed up the computational efficiency. The simulation and experiment results have verified the effectiveness and accuracy of the algorithm. Compared with the original one, this proposed algorithm reduces time consumption by 18%-34% and considerably saves memory space. |
| Key words: RBPF non-intact particle data SLAM |
| DOI：10.11916/j.issn.1005-9113.17064 |
| Clc Number:TP301.1 |
| Fund: |
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| Descriptions in Chinese: |
| 一种应用于FastSLAM的非完整数据RBPF算法 陈炜楠，管贻生，张宏，朱蕾 （广东工业大学 机电工程学院， 广州 510006） 创新点说明：本文研究了SLAM问题中的RBPF算法，进行粒子数据分离，根据权重将粒子个体的携带数据分为完整及非完整两个集合，以实现RBPF算法中数据存储量的控制；并通过对非完整数据粒子的数据补全，实现对原有算法框架的兼容。通过实验证明本算法有效提升了原有算法效率。 研究目的： 尝试解决SLAM问题中RBPF算法数据量巨大，时间耗时长问题。 研究方法： 使用FastSLAM 2.0作为研究基础，利用激光雷达SLAM数据集以及实际实验数据进行算法验证，以普通计算机作为运行平台统计算法的精度以及运行效率，以验证本研究工作。 结果： 实验证明，在相同算法耗时内，本算法可通过增加粒子数目，以提高SLAM结果精度（13%）；在相同粒子数目条件下，本算法可在一定幅度降低精度情况下（16%）大幅度提升（34%）算法效率 结论： 本算法通过对粒子集合进行分类以及数据分离，实现RBPF算法效率的提升，并随着粒子规模的增大，该提升优势更为明显；同时，通过对非完整粒子集合的数据补全，实现对原有算法框架的兼容。 关键词：RBPF，非完整粒子数据，SLAM |
