激光与光电子学进展, 2018, 55 (10): 101201, 网络出版: 2018-10-14
基于工作空间测量定位系统和激光雷达的室内组合导航算法研究 下载: 773次
Indoor Integrated Navigation Algorithm Based on Workshop Measurement Positioning System and Lidar
测量 工作空间测量定位系统 粒子滤波 卡尔曼滤波 激光雷达 组合导航 measurement workshop Measurement Positioning System particle filter Kalman filter lidar integrated navigation
摘要
针对复杂场景下大空间室内高精度导航应用, 提出一种基于工作空间测量定位系统(wMPS)和激光雷达的组合建图、定位算法。采用wMPS对激光雷达位姿进行精确估计, 融合激光雷达点云数据完成栅格地图的绘制, 使得机器人在导航时能够识别周围环境信息; 考虑到导航时容易缺失wMPS测量信息的情况, 根据雷达获得的实时数据与栅格地图通过粒子滤波算法反算雷达的位姿; 最后将粒子滤波的结果进行线性卡尔曼滤波处理, 并进行算法仿真与实验。仿真与实验结果表明:组合导航系统保证了地图的可靠性和动态导航精度, 大大提升了系统的整体性能。
Abstract
Aiming at the large space indoor high-precision navigation application in complex environment, we propose an algorithm for integrated mapping and positioning based on workshop Measurement Positioning System (wMPS) and lidar. The wMPS carries out precise estimation on the position and orientation of the lidar, and integrates the point cloud data of the lidar to complete the mapping of the grid map. Therefore, the robot can recognize the information of the surrounding environment when navigating. Then considering that the wMPS measurement information is easy to be missing during navigating, we use particle filter algorithm to inversely calculate the position and orientation of the lidar according to the real-time point cloud data from lidar and the gird map. Finally, the results of particle filter are processed with the linear Kalman filter, and the algorithm simulation and experiment are conducted. The simulation and experimental results show that the integrated navigation system ensures the reliability of the map and the accuracy of the dynamic navigation, which greatly improves the overall performance of the navigation system.
王金旺, 杨凌辉, 史慎东, 赵显, 张正吉, 徐秋宇. 基于工作空间测量定位系统和激光雷达的室内组合导航算法研究[J]. 激光与光电子学进展, 2018, 55(10): 101201. Wang Jinwang, Yang Linghui, Shi Shendong, Zhao Xian, Zhang Zhengji, Xu Qiuyu. Indoor Integrated Navigation Algorithm Based on Workshop Measurement Positioning System and Lidar[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101201.