摘要
【目的】旨在解决敬老院智能机器人SLAM建图存在的边缘错位、建图空白、全局地图不一致的问题。【方法】通过研究对比Gmapping、ORB-SLAM2、RTAB-Map建图算法,提出利用GTSAM的位姿图全局优化机制、非线性最小二乘约束求解、构建扩展卡尔曼滤波(EKF)多传感器融合架构来优化RTAB-Map建图算法。采用优化后的RTAB-Map建图算法在真实环境下融合单线激光雷达、IMU、RGB-D等3种传感器的数据进行实地建图。【结果】试验结果显示,机器人建图轨迹漂移率降低68%,机器人状态估计误差下降42%,室内狭小空间建图边缘对齐误差≤5 cm,弱光场景地图完整度提升89%且动态干扰下系统稳定性也有所提升,环境建图过程中对于缓慢行动的老人(动态物体)过滤性能提升50%。【结论】改进后的RTAB-Map算法可以解决机器人在敬老院SLAM建图过程中遇到的边缘错位、建图空白、全局地图不一致的问题,为机器人在敬老院的应用提供一定的技术支撑。
[Purposes]This study aims to solve the problems of edge misalignment,mapping blanks,and inconsistent global maps in the SLAM mapping of intelligent robots in gerocomiums.[Methods]By researching and comparing the Gmapping,ORB-SLAM2,and RTAB-Map mapping algorithms,we propose an optimized RTAB-Map algorithm.The optimization incorporates the global pose graph optimization mechanism of GTSAM,nonlinear least-squares constraint solving,and an Extended Kalman Filter(EKF)multi-sensor fusion architecture.The optimized RTAB-Map algorithm is then used to perform field mapping in real environments by fusing data from three sensors:a single-line LiDAR,an IMU,and an RGB-D camera.[Findings]Experimental results show that the robot's mapping trajectory drift rate is reduced by 68%,the robot's state estimation error is decreased by 42%,the edge alignment error in narrow indoor spaces is≤5 cm,the map completeness in low-light scenarios is increased by 89%,and the system stability under dynamic interference is improved.Furthermore,the filtering performance for slowmoving elderly individuals(dynamic objects)during mapping is improved by 50%.[Conclusions]The improved RTAB-Map algorithm can solve the problems of edge misalignment,mapping blanks,and inconsistent global maps encountered by robots during SLAM mapping in gerocomiums,providing technical support for the application of robots in such settings.
作者
王小涛
张洪艳
刘惠杰
张海涛
孙仁杰
WANG Xiaotao;ZHANG Hongyan;LIU Huijie;ZHANG Haitao;SUN Renjie(Jilin University of Chemical Technology,Jilin 132011,China)
出处
《河南科技》
2025年第24期22-27,共6页
Henan Science and Technology
基金
吉林省教育厅产业化培育项目(JJKH20240313CY)。
