融合Wi-Fi与激光的机器人室内大型环境SLAM  

Large-scale indoor environment SLAM for mobile robot based on Wi-Fi and LiDAR

作　　者：熊壮 刘冉[1,2] 郭林 肖宇峰[1,2] Xiong Zhuang;Liu Ran;Guo Lin;Xiao Yufeng(School of Information Engineering,Southwest University of Science&Technology,Mianyang Sichuan 621000,China;Robot Technology Used for Special Environment Key Laboratory of Sichuan Province,Southwest University of Science&Technology,Mianyang Sichuan 621000,China)

机构地区：[1]西南科技大学信息工程学院,四川绵阳621000 [2]西南科技大学特殊环境机器人四川省重点实验室,四川绵阳621000

出　　处：《计算机应用研究》2025年第3期812-817,共6页Application Research of Computers

基　　金：四川省科技计划资助项目(2023NSFSC0505,2022YFG0242);国家自然科学基金资助项目(12175187,12205245)。

摘　　要：同步定位与地图构建(SLAM)是实现移动机器人自主导航定位的关键。针对室内大型环境下激光SLAM闭环检测容易产生错误闭环,导致机器人位姿估计误差较大的问题,提出了一种融合Wi-Fi与激光信息的图优化SLAM算法。首先,构建Wi-Fi指纹序列与激光子地图;然后,根据每对指纹序列的相似度均值和标准差筛选用于闭环检测的激光子地图。在此基础上,提取所筛选子地图的特征点并匹配,以确定激光闭环;最后,通过图优化方法融合里程计与激光闭环,优化机器人的轨迹并构建全局地图。在170 m×30 m和180 m×80 m的室内环境中采集了三组数据,对所提算法性能进行验证。实验结果显示,所提算法的定位精度在三组数据上分别达到0.78 m、0.67 m和0.89 m,与激光SLAM算法相比分别提升了48.6%、53.1%和68.7%,证明所提算法有效提高了室内大型环境下激光SLAM的位姿估计精度。SLAM is crucial for the autonomous navigation and positioning of robots.Aiming at the problem of considerable pose estimation errors for robots,which were caused by incorrect loop closures in LiDAR SLAM within large-scale indoor environments,this paper proposed a graph-based SLAM algorithm that fused Wi-Fi and LiDAR information.Initially,the algorithm constructed Wi-Fi fingerprint sequences and LiDAR submaps.Subsequently,it selected LiDAR submaps for loop closure detection based on the mean and standard deviation of similarity between each pair of fingerprint sequences.Then,it extracted feature points from the selected submaps and matched them to confirm LiDAR loop closure.Ultimately,using a graph optimization approach,odometry and LiDAR loop closures were fused to optimize the robot’s trajectory and construct a global map.Three datasets were collected in 170 m×30 m and 180 m×80 m indoor environments to verify the performance of the proposed algorithm.The experimental results show that positioning accuracy values of proposed algorithm in three datasets reach 0.78 m,0.67 m,and 0.89 m,which give improvements of 48.6%,53.1%,and 68.7%when compared to the LiDAR SLAM algorithm,demonstrating its effectiveness for enhancing pose estimation accuracy in large-scale indoor environments.

关 键 词：Wi-Fi指纹序列 激光子地图筛选 闭环检测 图优化 同步定位与地图构建 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]