An Fusion SLAM Method for LiDAR Visual and IMU Based on Factor Map Elimination Optimization

YUAN Guo-shuai; QI Yong-sheng; LIU Li-qiang; SU Jian-qiang; ZHANG Li-jie

doi:10.12263/DZXB.20230209

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An Fusion SLAM Method for LiDAR Visual and IMU Based on Factor Map Elimination Optimization

PAPERS | 更新时间：2025-12-08

- An Fusion SLAM Method for LiDAR Visual and IMU Based on Factor Map Elimination Optimization
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3042-3052(2023)
- 作者机构：
  
  1.内蒙古工业大学电力学院，内蒙古呼和浩特 010080
  2.内蒙古自治区高等学校智慧能源技术与装备工程研究中心，内蒙古呼和浩特 010080
  3.大规模储能技术教育部工程研究中心，内蒙古呼和浩特 010080
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62241309);Inner Mongolia Science and Technology Project(2020GG028;2021GG164);Inner Mongolia Natural Science Foundation(2020MS05029;2021MS06018)
- DOI：10.12263/DZXB.20230209
  CLC： TP242.6
- Received：09 March 2023，
  
  Revised：2023-06-26，
  
  Published：25 November 2023
- 稿件说明：
移动端阅览
袁国帅,齐咏生,刘利强等.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.

YUAN Guo-shuai,QI Yong-sheng,LIU Li-qiang,et al.An Fusion SLAM Method for LiDAR Visual and IMU Based on Factor Map Elimination Optimization[J].ACTA ELECTRONICA SINICA,2023,51(11):3042-3052.
袁国帅,齐咏生,刘利强等.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. DOI： 10.12263/DZXB.20230209.

YUAN Guo-shuai,QI Yong-sheng,LIU Li-qiang,et al.An Fusion SLAM Method for LiDAR Visual and IMU Based on Factor Map Elimination Optimization[J].ACTA ELECTRONICA SINICA,2023,51(11):3042-3052. DOI： 10.12263/DZXB.20230209.

摘要

针对单一传感器SLAM（Simultaneous Localization And Mapping）技术在复杂环境中存在精度低、可靠性差等问题，提出一种基于因子图消元优化的激光雷达、视觉和IMU（Inertial Measurement Unit）融合SLAM算法（Multi Factor Graph fusion SLAM with IMU as the Dominant system，ID-MFG-SLAM）.首先，采用多因子图模型，提出以IMU为主系统，视觉与激光雷达为辅系统，通过引入辅系统观测因子约束IMU偏差，并接收IMU里程计因子实现运动预测与融合的全新结构.之后，为降低融合后的优化成本，加入滑窗机制并设计基于Householder变换的QR分解消元法将因子图转换为贝叶斯网络.最后，引入一种球面线性插值与线性插值之间的自适应插值算法，将激光雷达点云投影到单位球体上实现视觉特征点深度估计.实验结果表明，相比其他经典算法，该方法在复杂大、小场景中绝对轨迹误差分别可达到约0.68 m和0.24 m，具有更高的精度和可靠性.

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 novel structure incorporates observation factors from the secondary systems to constrain IMU biases and integrates IMU odometry factors for motion prediction and fusion. To reduce the optimization cost after fusion

a sliding window mechanism is introduced 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 structure and improving computational efficiency. Furthermore

an adaptive interpolation algorithm between quaternion spherical 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 algorithms

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

Keywords

references

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