计算机应用研究2024，Vol.41Issue(4)：1247-1251,5.DOI:10.19734/j.issn.1001-3695.2023.08.0356
基于多传感信息融合的语义词袋SLAM优化算法
Multi-sensor information fusion SLAM based on semantic word bags
摘要
Abstract
This paper proposed an algorithm known as MSW-SLAM(multi-sensor information fusion SLAM based on semantic word bags)to address the issue of inaccurate LiDAR odometry position and pose calculations in the mapping of outdoor large-scale environments by mobile robots,resulting in a decrease in the accuracy of the simultaneous localization and mapping(SLAM)algorithm.This algorithm incorporated raw observation data from LiDAR using a visual inertial system and conducts joint nonlinear optimization of measurements from the inertial measurement unit(IMU),visual features,and laser point cloud features using sliding windows,the algorithm leveraged the complementary semantic word bag characteristics of vision and Li-DAR for closed-loop optimization,further enhancing the global positioning and mapping accuracy of the multi-sensor fusion SLAM system.Experimental results demonstrate that,compared to traditional tightly coupled binocular vision-inertial odometry and LiDAR odometry positioning,the MSW-SLAM algorithm can effectively detect closed-loop information in trajectories and achieve high-precision global pose optimization.The point cloud map after closed-loop detection exhibits excellent resolution and global consistency.关键词
同时定位与实时建图/语义词袋/位姿估计Key words
simultaneous positioning and real-time mapping/semantic word bag/pose estimation分类
信息技术与安全科学引用本文复制引用
袁鹏,谷志茹,刘中伟,焦龙飞,毛麒云..基于多传感信息融合的语义词袋SLAM优化算法[J].计算机应用研究,2024,41(4):1247-1251,5.基金项目
湖南省自然科学基金资助项目(2022JJ50005) （2022JJ50005）
湖南省研究生科研创新项目(QL20230216) （QL20230216）
国家自然科学基金区域联合基金重点项目(U23A20385) （U23A20385）
