电子学报2023，Vol.51Issue(11)：3042-3052,11.DOI:10.12263/DZXB.20230209
一种基于因子图消元优化的激光雷达视觉惯性融合SLAM方法
An Fusion SLAM Method for LiDAR Visual and IMU Based on Factor Map Elimination Optimization
摘要
Abstract
Addressing the limitations of single-sensor SLAM(Simultaneous Localization And Mapping)techniques,degraded perception,and poor reliability in complex environments,this paper proposes a multi-factor graph fusion SLAM algorithm with IMU as the dominant system(ID-MFG-SLAM).Firstly,the utilization of a multi-factor graph model,with the IMU(Inertial Measurement Unit)as the primary system and visual and LIDAR sensors as secondary systems.This nov-el structure incorporates observation factors from the secondary systems to constrain IMU biases and integrates IMU odome-try factors for motion prediction and fusion.To reduce the optimization cost after fusion,a sliding window mechanism is in-troduced for historical state information backtracking.Additionally,a QR decomposition elimination method based on Householder transformation is employed to convert the factor graph into a Bayesian network,simplifying the graph's struc-ture and improving computational efficiency.Furthermore,an adaptive interpolation algorithm between quaternion spheri-cal linear interpolation and linear interpolation is introduced.This algorithm projects LIDAR point clouds onto a unit sphere,enabling depth estimation of visual feature points.The experimental results show that compared to other classic al-gorithms,this method can achieve absolute trajectory errors of about 0.68 m and 0.24 m in complex large and small scenes,respectively,with higher accuracy and reliability.关键词
同时定位与建图/多传感器融合/复杂场景/激光雷达/IMU里程计/因子图优化Key words
simultaneous localization and mapping/multi-sensor fusion/complex scene/laser radar/IMU odometer/factor graph optimization分类
信息技术与安全科学引用本文复制引用
袁国帅,齐咏生,刘利强,苏建强,张丽杰..一种基于因子图消元优化的激光雷达视觉惯性融合SLAM方法[J].电子学报,2023,51(11):3042-3052,11.基金项目
国家自然科学基金(No.62241309) （No.62241309）
内蒙古科技计划项目(No.2020GG028,No.2021GG164) （No.2020GG028,No.2021GG164）
内蒙古自然科学基金(No.2020MS05029,No.2021MS06018)National Natural Science Foundation of China(No.62241309) （No.2020MS05029,No.2021MS06018）
Inner Mongolia Science and Technology Project(No.2020GG028,No.2021GG164) （No.2020GG028,No.2021GG164）
Inner Mongolia Natural Science Foundation(No.2020MS05029,No.2021MS06018) （No.2020MS05029,No.2021MS06018）
