IRBPF-SLAM Algorithm for Intelligent Resampling of a Mobile Robots
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摘要: 为了提高激光SLAM技术的建图精度，本文提出了一种智能重采样的IRBPF-SLAM算法，算法采用BAT启发式自适应重采样，对小颗粒进行重采样，产生新的解决方案，随机选择最佳解决方案，更新蝙蝠数量，将粒子归一化，优化的机器人状态更新，最后进行粒子重置，其迭代时间可以根据滤波器发散的程度进行自适应调整。此外，将激光传感器改进提议分布融合到算法中，以获得更好的提议分布和建图结果。仿真结果表明，所提出的IRBPF具有更好的准确性、计算效率和滤波一致性。在大型室内空间中，将IRBPF-SALM算法融合到基于ROS为框架下，在阿克曼转向移动平台进行测试，测试结果表明了新方法比原始方法更具优势。
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关键词:
- 智能重采样 /
- IRBPF-SLAM /
- 启发式 /
- 归一化 /
- 自适应调整
Abstract: In order to improve the drawing accuracy of laser SLAM technology, this paper proposed an IRBPF-SLAM algorithm for intelligent resampling. The algorithm uses the bat heuristic adaptive resampling to resample small particles, generate new solutions, randomly select the best solution, update the number of bats, normalize the particles, update the optimal state of the robot, and finally reset the particles. The iteration times of the algorithm can be adjusted adaptively according to the degree of filter divergence. In addition, the distribution of laser sensors improved in the paper is integrated into the algorithm to obtain better distribution and mapping results. Simulation results show that the IRBPF-SLAM algorithm has better accuracy, calculation efficiency and filter consistency. In a large indoor space, the IRBPF-SLAM algorithm is integrated into the framework of ROS, and tested on the Ackerman mobile platform. The test results show that the new algorithm has more advantages than other algorithms. -
表 1 算法参数解释
参数 数值 第i时刻蝙蝠初始响度$A_i^0$ 0.9 常数$\alpha ,\gamma $ 0.9 第i时刻蝙蝠初始心率$r_i^0$ 0.1 随机向量$\boldsymbol{\varepsilon}$ 0.001 频率范围$[ {{f_{\min }},{f_{\max }}} ]$ [0，1] 迭代时间范围$[ {{N_{\min }},{N_{\max }}} ]$ [3，10] 
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