基于IESKF与图优化的激光惯性SLAM算法
LiDAR Inertial SLAM Algorithm Based on IESKF with Factor Graph Optimization
魏志飞 1樊绍胜 1熊铭轩1
作者信息
- 1. 长沙理工大学电气与信息工程学院,湖南 长沙 410000
- 折叠
摘要
同步定位与建图(SLAM)算法在使用激光雷达建图过程中,由于环境的变化且配准参数固定,会在点云配准过程中产生较大的累计误差,并且对高程误差抑制效果不佳,进而会影响全局的配准精度和建图定位效果.针对上述问题,提出一种基于迭代误差状态卡尔曼滤波器(IESKF)与因子图优化的激光惯性SLAM算法.该算法包括自适应参数调节模块、点云预处理模块、前端里程计模块和后端因子图优化模块.根据点云规模确定不同的关键帧距离、配准、体素下采样,以及地面约束等参数.采用K近邻(KNN)选取关键帧组成局部地图,可充分利用帧-图匹配时的空间信息.通过IESKF融合点云残差与惯性测量单元(IMU)先验位姿,得到滤波方式的前端融合里程计.在后端优化中加入地面约束、回环约束构成因子图优化,提高了建图的全局一致性.在M2DGR公开数据集和实际场景上进行多算法对比实验,实验结果表明,在校园场景中:相较于仅使用因子图优化的LIO-SAM算法,所提算法在全局建图精度上提高了38%,在高程误差上减少了52%;相较于仅使用IESKF的FAST-LIO2算法,所提算法在全局建图精度上提高了64%,在高程误差上减少了62%,证明其在环境适应性和高程误差抑制方面具备更优异的性能.
Abstract
Simultaneous localization and mapping(SLAM)algorithm in the process of mapping using LiDAR,due to the changes in the environment and the fixed alignment parameters,it will produce a large cumulative error in the process of point cloud alignment,and the poor suppression of elevation error,which in turn will affect the global alignment accuracy and mapping positioning effect.Aiming at the above problems,a laser inertial SLAM algorithm based on iterative error state Kalman filter(IESKF)and factor map optimization is proposed,with an adaptive parameter adjustment module,a point cloud preprocessing module,a front-end odometry module,and a back-end factor map optimization module.According to the size of point cloud,different key frame distance parameters,alignment parameters,voxel downsampling parameters and ground constraints are decided;K-nearest neighbor(KNN)is used to select key frames to compose local maps,which makes full use of the spatial information in frame-map matching;the point cloud residuals are fused with IMU through the IESKF,which is an adaptive algorithm.The IESKF fuses the point cloud residuals with the a priori position of IMU to obtain the front-end odometry of the filter fusion method;the ground constraints are added in the back-end optimization and combined with the loopback constraints to form the factor map optimization,which improves the global consistency of the map construction.Multi-algorithm comparison experiments are carried out on the M2DGR public dataset and real scenarios,and the experimental results show that,in real scenarios,the proposed algorithm improves the global mapping accuracy by 38%,and reduces the elevation error by 52%compared with the LIO-SAM algorithm that only uses factor graph optimization,and improves the global mapping accuracy by 64%,and reduces the elevation error by 62%compared with the FAST-LIO2 algorithm that only uses IESKF.The results demonstrate that the proposed algorithm has better performance in terms of environmental adaptability and elevation error suppression.
关键词
同步定位与建图/动态参数调节器/迭代误差状态卡尔曼滤波器/因子图优化/自适应关键帧Key words
simultaneous localization and mapping/dynamic parameter regulator/iterative error state Kalman filter/factor graph optimization/adaptive keyframe引用本文复制引用
基金项目
国家自然科学基金(62271087)
湖南省研究生科研创新项目(CX20220918)
出版年
2024
NETL
NSTL
万方数据