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
- 电子学报 2023年51卷第11期 页码：3042-3052
- 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：
[ "袁国帅 男，1995年10月出生于山东省菏泽市.现为内蒙古工业大学电力学院硕士研究生.主要研究方向为移动机器人智能控制. E-mail: 1500523977@qq.com" ]
[ "齐咏生（通讯作者） 男，1975年12月出生于内蒙古包头市.现为内蒙古工业大学电力学院教授、副院长.主要研究方向为移动机器人协同控制技术." ]
[ "刘利强 男，1975年5月出生于内蒙古包头市.现为内蒙古工业大学电力学院教授、硕士生导师.主要研究方向为计算机视觉、新能源发电技术.E-mail: llqiang@imut.edu.cn" ]
[ "苏建强 男，1983年10月出生于内蒙古乌拉特前旗.现为内蒙古工业大学电力学院副教授、硕士生导师.主要研究方向为机器人智能感知与运动控制. E-mail: feiyang@imut.edu.cn" ]
[ "张丽杰 女，1973年12月出生于辽宁省康平县.现为内蒙古工业大学电力学院教授、硕士生导师.主要研究方向为导航、检测技术与自动控制.中国电子学会会员编号：E190102064M.E-mail: zhanglijie@imut.edu.cn" ]
- 基金信息：国家自然科学基金(62241309);内蒙古科技计划项目(2020GG028;2021GG164);内蒙古自然科学基金(2020MS05029;2021MS06018)
DOI：10.12263/DZXB.20230209
中图分类号： TP242.6收稿：2023-03-09，
修回：2023-06-26，
纸质出版：2023-11-25
- 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
BAILEY T , DURRANT-WHYTE H . Simultaneous localization and mapping (SLAM): Part II [J ] . IEEE Robotics & Automation Magazine , 2006 , 13 ( 3 ): 108 - 117 .
ZHANG J , SINGH S . Low-drift and real-time LiDAR odometry and mapping [J ] . Autonomous Robots , 2017 , 41 ( 2 ): 401 - 416 .
SHAN T X , ENGLOT B . LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain [C ] // 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway : IEEE , 2019 : 4758 - 4765 .
MUR-ARTAL R , MONTIEL J M M , TARDÓS J D . ORB-SLAM: A versatile and accurate monocular SLAM system [J ] . IEEE Transactions on Robotics , 2015 , 31 ( 5 ): 1147 - 1163 .
MUR-ARTAL R , TARDÓS J D . ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras [J ] . IEEE Transactions on Robotics , 2017 , 33 ( 5 ): 1255 - 1262 .
TUBMAN R , POTGIETER J , ARIF K M . Efficient robotic SLAM by fusion of RatSLAM and RGBD-SLAM [C ] // 2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP) . Piscataway : IEEE , 2017 : 1 - 6 .
LIU Z X , XIE C X , XIE M , et al . Mobile robot positioning method based on multi-sensor information fusion laser SLAM [J ] . Cluster Computing , 2019 , 22 ( 2 ): 5055 - 5061 .
LIN J R , ZHENG C R , XU W , et al . R 2 LIVE: A robust, real-time, LiDAR-inertial-visual tightly-coupled state estimator and mapping [J ] . IEEE Robotics and Automation Letters , 2021 , 6 ( 4 ): 7469 - 7476 .
LIN J R , ZHANG F . R 3 LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package [C ] // 2022 International Conference on Robotics and Automation (ICRA) . Piscataway : IEEE , 2022 : 10672 - 10678 .
SHAN T X , ENGLOT B , MEYERS D , et al . LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping [C ] // 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway : IEEE , 2021 : 5135 - 5142 .
XU W , ZHANG F . FAST-LIO: A fast, robust LiDAR-inertial odometry package by tightly-coupled iterated Kalman filter [J ] . IEEE Robotics and Automation Letters , 2021 , 6 ( 2 ): 3317 - 3324 .
XU W , CAI Y X , HE D J , et al . FAST-LIO2: Fast direct LiDAR-inertial odometry [J ] . IEEE Transactions on Robotics , 2022 , 38 ( 4 ): 2053 - 2073 .
QIN T , LI P L , SHEN S J . VINS-mono: A robust and versatile monocular visual-inertial state estimator [J ] . IEEE Transactions on Robotics , 2018 , 34 ( 4 ): 1004 - 1020 .
QIN T , PAN J , CAO S Z , et al . A general optimization-based framework for local odometry estimation with multiple sensors [EB/OL ] . ( 2019-01-11 )[ 2023-02-20 ] . https://api.semanticscholar.org/CorpusID:57825758 https://api.semanticscholar.org/CorpusID:57825758 .
CAMPOS C , ELVIRA R , RODRIGUEZ J J G , et al . ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM [J ] . IEEE Transactions on Robotics , 2021 , 37 ( 6 ): 1874 - 1890 .
ZHANG J , SINGH S . Visual-LiDAR odometry and mapping: Low-drift, robust, and fast [C ] // 2015 IEEE International Conference on Robotics and Automation (ICRA) . Piscataway : IEEE , 2015 : 2174 - 2181 .
SHAO W Z , VIJAYARANGAN S , LI C , et al . Stereo visual inertial LiDAR simultaneous localization and mapping [C ] // 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway : IEEE , 2020 : 370 - 377 .
SHAN T X , ENGLOT B , RATTI C , et al . LVI-SAM: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping [C ] // 2021 IEEE International Conference on Robotics and Automation (ICRA) . Piscataway : IEEE , 2021 : 5692 - 5698 .
YIN J E , LI A , LI T , et al . M2DGR: A multi-sensor and multi-scenario SLAM dataset for ground robots [J ] . IEEE Robotics and Automation Letters , 2022 , 7 ( 2 ): 2266 - 2273 .
HSU L T , KUBO N , WEN W S , et al . UrbanNav: An open-sourced multisensory dataset for benchmarking positioning algorithms designed for urban areas [C ] // Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation . Missouri : Institute of Navigation , 2021 : 226 - 256 .
FORSTER C , CARLONE L , DELLAERT F , et al . On-manifold preintegration for real-time visual: Inertial odometry [J ] . IEEE Transactions on Robotics , 2017 , 33 ( 1 ): 1 - 21 .
CARLONE L , ARAGUES R , CASTELLANOS J A , et al . A fast and accurate approximation for planar pose graph optimization [J ] . The International Journal of Robotics Research , 2014 , 33 ( 7 ): 965 - 987 .
DELLAERT F , KAESS M . Factor graphs for robot perception [J ] . Foundations and Trends in Robotics , 2017 , 6 ( 1/2 ): 1 - 139 .
DU S Y , ZHENG N N , XIONG L , et al . Scaling iterative closest point algorithm for registration of m-D point sets [J ] . Journal of Visual Communication and Image Representation , 2010 , 21 ( 5/6 ): 442 - 452 .
LI Y Y , FAN S Y , SUN Y B , et al . Bundle adjustment method using sparse BFGS solution [J ] . Remote Sensing Letters , 2018 , 9 ( 8 ): 789 - 798 .
ZACH C . Robust bundle adjustment revisited [C ] // Computer Vision — ECCV 2014 . Cham : Springer International Publishing , 2014 : 772 - 787 .
0
浏览量
21
下载量
3
CSCD
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies