[1] Wang J K, Zuo X X, Zhao X R et al. Review of multi-source fusion SLAM: current status and challenges[J]. Journal of Image and Graphics, 27, 368-389(2022).

[2] Yang Y, Wang L Y, Sun P et al. LiDAR map construction in dynamic environment[J]. Computer Engineering and Design, 44, 2125-2131(2023).

[3] Zhou Z G, Cao J W, Di S F. Overview of 3D lidar SLAM algorithms[J]. Chinese Journal of Scientific Instrument, 41, 13-27(2021).

[4] Xue G H, Li R X, Zhang Z H et al. State-of-the-art and tendency of SLAM algorithms based on 3D LiDAR[J]. Information and Control, 52, 18-36(2023).

[5] Zhang J, Singh S. LOAM: lidar odometry and mapping in real-time[EB/OL]. https://www.roboticsproceedings.org/rss10/p07.pdf

[6] Shan T X, Englot B. LeGO-LOAM: lightweight and ground-optimized lidar odometry and mapping on variable terrain[C], 4758-4765(2018).

[7] Neuhaus F, Koß T, Kohnen R, Brox T, Bruhn A, Fritz M et al. MC2SLAM: real-time inertial lidar odometry using two-scan motion compensation[M]. Pattern recognition. Lecture notes in computer science, 11269, 60-72(2019).

[8] Koide K, Miura J, Menegatti E. A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement[J]. International Journal of Advanced Robotic Systems, 16, 1-13(2019).

[9] Shan T X, Englot B, Meyers D et al. LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping[C], 5135-5142(2020).

[10] Zhang L, Xu X B, Cao C F et al. Robot pose estimation method based on image and point cloud fusion with dynamic feature elimination[J]. Chinese Journal of Lasers, 49, 0610001(2022).

[11] 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, 6, 3317-3324(2021).

[12] Xu W, Cai Y X, He D J et al. FAST-LIO2: fast direct LiDAR-inertial odometry[J]. IEEE Transactions on Robotics, 38, 2053-2073(2022).

[13] Zhang Z Y, Zhou H X, Wang S et al. Research on algorithm of plane fitting of RANSAC-WTLSD[J]. Foreign Electronic Measurement Technology, 41, 93-98(2022).

[14] Vitali R V, McGinnis R S, Perkins N C. Robust error-state Kalman filter for estimating IMU orientation[J]. IEEE Sensors Journal, 21, 3561-3569(2021).

[15] Qin C, Ye H Y, Pranata C E et al. LINS: a lidar-inertial state estimator for robust and efficient navigation[C], 8899-8906(2020).

[16] Lin J R, Zheng C R, Xu W et al. R2 LIVE: a robust, real-time, LiDAR-inertial-visual tightly-coupled state estimator and mapping[J]. IEEE Robotics and Automation Letters, 6, 7469-7476(2021).

[17] Xu C, Zhang H X, Gu J. Scan context 3D lidar inertial odometry via iterated ESKF and incremental K-dimensional tree[C], 21-27(2022).

[18] Wang F B, Cao K, Gong X et al. LiDAR SLAM method for photovoltaic power station based on factor graph optimization[J]. Laser & Optoelectronics Progress, 61, 1615002(2024).

[19] Yuan C J, Lin J R, Zou Z H et al. STD: stable triangle descriptor for 3D place recognition[C], 1897-1903(2023).

[20] Besl P J, McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256(1992).

[21] Yin J, Li A, Li T et al. M2DGR: a multi-sensor and multi-scenario SLAM dataset for ground robots[J]. IEEE Robotics and Automation Letters, 7, 2266-2273(2022).

[22] Sturm J, Engelhard N, Endres F et al. A benchmark for the evaluation of RGB-D SLAM systems[C], 573-580(2012).