自恢复蒙特卡罗定位算法  

Self-recovery Monte Carlo localization algorithm

作　　者：陈铭 张淑芳 缪长蔚 李亚阳 CHEN Ming;ZHANG Shufang;MIAO Changwei;LI Yayang(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学电气自动化与信息工程学院,天津300072

出　　处：《光电子．激光》2023年第1期43-51,共9页Journal of Optoelectronics·Laser

摘　　要：机器人定位技术作为智能机器人领域的重要技术,是机器人进行自主规划和导航的重要前提。为解决机器人运动过程中的绑架问题,在蒙特卡罗定位(Monte Carlo localization, MCL)算法的基础上,提出了基于激光雷达似然域模型的定位可靠度评判算法以及基于惯性导航单元的定位自恢复模型。定位可靠度评判算法对机器人是否发生绑架问题进行判定,当发生绑架问题后,首先基于惯性导航单元的测量数据进行位姿预估计,然后基于预估计的位姿构建粒子重分布模型,最后进行粒子滤波得到重定位的结果,达到了对机器人绑架判定和自恢复定位的目的。经过实验测试和对比,该算法可以对绑架问题进行高效的判断,具有更高的恢复效率和准确度。As an important technology in the field of intelligent robots, robot positioning technology is an important prerequisite for autonomous planning and navigation of robots. In order to solve the kidnapping problem in the process of robot motion, based on the Monte Carlo localization(MCL) algorithm, a positioning reliability evaluation algorithm based on the LiDAR likelihood domain model and a positioning self-recovery model based on the inertial navigation unit are proposed.The positioning reliability evaluation algorithm determines whether the robot has a kidnapping problem. When the kidnapping problem occurs, it first pre-estimates the pose based on the measurement data of the inertial navigation unit, then builds a particle redistribution model based on the pre-estimated pose, and finally performs particle filtering to obtain the relocation result, which achieves the purpose of determining the robot abduction and self-recovery localization. After experimental testing and comparison, the algorithm can effectively judge the kidnapping problem, and has higher recovery efficiency and accuracy.

关 键 词：机器人 激光雷达 惯性导航 定位恢复 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]