[1] Smith R C, Cheeseman P. On the representation and estimation of spatial uncertainly[J]. International Journal of Robotics Research, 5, 56-68(1986).

[2] Cadena C, Carlone L, Carrillo H et al. Past, present, and future of simultaneous localization and mapping: toward the robust-perception age[J]. IEEE Transactions on Robotics, 32, 1309-1332(2016).

[3] Wang H, Wang C, Chen C L et al. F-LOAM: fast LiDAR odometry and mapping[C], 4390-4396(2021).

[4] Chen M Y, Ding L M, Zhang Y K. Fast PL-SLAM algorithm based on improved keyframe extraction strategy[J]. Acta Electronica Sinica, 50, 608-618(2022).

[5] Liu M Z, Xu G H, Tang T et al. Review of SLAM based on lidar[J]. Computer Engineering and Applications, 60, 1-14(2024).

[6] Yuan G S, Qi Y S, Liu L Q et al. An fusion SLAM method for LiDAR visual and IMU based on factor map elimination optimization[J]. Acta Electronica Sinica, 51, 3042-3052(2023).

[7] Xu X S, Li N, Yao Y Q. Lidar SLAM algorithm in outdoor environment based on fast loop detection[J]. Journal of Chinese Inertial Technology, 30, 716-722(2022).

[8] Zhang J, Singh S. LOAM: lidar odometry and mapping in real-time[EB/OL]. https:∥doi.org/10.15607/RSS.2014.X.007?sid=semanticscholar

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

[10] Mei S M, Huang M H, Liu Z H et al. Ground segmentation method in complex scenes based on three-dimensional lidar[J]. Laser & Optoelectronics Progress, 59, 1028003(2022).

[11] Shan T X, Englot B, Meyers D et al. LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping[C], 5135-5142(24).

[12] Zeng Q X, Li Q, Nie W Z. Lidar SLAM algorithm based on online point cloud removal in pseudo occupied area[J]. Acta Optica Sinica, 43, 2028001(2023).

[13] Pomerleau F, Colas F, Siegwart R et al. Comparing ICP variants on real-world data sets[J]. Autonomous Robots, 34, 133-148(2013).

[14] Lever J, Krzywinski M, Altman N. Principal component analysis[J]. Nature Methods, 14, 641-642(2017).

[15] Lim H, Oh M, Myung H. Patchwork: concentric zone-based region-wise ground segmentation with ground likelihood estimation using a 3D LiDAR sensor[J]. IEEE Robotics and Automation Letters, 6, 6458-6465(2021).

[16] Jiang K, Zhang X L, Qin B J et al. Feature-based loop closure detection and optimization for LiDAR mapping[R], 01-5225(2020).

[17] Wang Y, Sun Z Z, Xu C Z et al. LiDAR iris for loop-closure detection[C], 5769-5775(24).

[18] Lim H, Yeon S, Ryu S et al. A single correspondence is enough: robust global registration to avoid degeneracy in urban environments[C], 8010-8017(2022).

[19] Zhou Q Y, Park J, Koltun V. Fast global registration[M]. Computer vision‒ECCV 2016, 9906, 766-782(2016).

[20] Black M J, Rangarajan A. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision[J]. International Journal of Computer Vision, 19, 57-91(1996).

[21] Scaramuzza D. 1-point-RANSAC structure from motion for vehicle-mounted cameras by exploiting non-holonomic constraints[J]. International Journal of Computer Vision, 95, 74-85(2011).

[22] Zhang J C, Wen W S, Hsu L T et al. LiDAR feature outlier mitigation aided by graduated Non-convexity relaxation for safety-critical localization in urban canyons[C], 660-664(2023).

[23] Tzoumas V, Antonante P, Carlone L. Outlier-robust spatial perception: hardness, general-purpose algorithms, and guarantees[C], 5383-5390(2019).

[24] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C], 3354-3361(2012).

[25] 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).