基于Rao-Blackwellized粒子滤波器移动机器人SLAM研究

Mobile Robot SLAM Based on Rao-Blackwellized Particle Filter

中文关键词:  同时定位和建图  激光雷达  RBPF-SLAM  机器人操作系统

英文关键词:simultaneous localization and mapping  LiDAR  RBPF-SLAM  robot operating system

中文摘要:

      SLAM是移动机器人在未知环境下实现自主导航的关键技术，为解决传统RBPF-SLAM算法建图效果差、计算效率低的不足，基于分层控制的思想，利用kobuki底盘和RPLIDAR A2雷达搭建了机器人导航系统，提出一种优化的Rao-Blackwellized粒子滤波的SLAM方法，粒子采样时纳入高精度的激光数据以弥补里程计数据的不足，优化建议分布函数，对相邻扫描帧进行迭代最近点匹配，增加自适应重采样步骤，并进行了现场建图实验。对比定位误差和运行效率，改进方法要优于传统方法，表明改进方法能有效解决上述问题。

英文摘要:

      SLAM is a key technology for mobile robots to realize autonomous navigation in unknown environments.To solve the problems of poor mapping and low computational efficiency of the traditional RBPF-SLAM algorithm,a robot navigation system was built by using the kobuki chassis and RPLIDAR A2 radar based on the idea of hierarchical control,and an optimized Rao-Blackwellized particle filter SLAM method was proposed.High-precision laser data was incorporated into the particle sampling to make up for the deficiencies of the odometer data.The proposed distribution function was optimized,and the nearest scan point was iterated.The adaptive resampling step was added,and the on-site mapping experiments were carried out.The comparison of positioning error and operational efficiency shows that the improved method is superior to the traditional method,and it is concluded that the improved method can effectively solve the above problems.