[1] Xu X B, Zhang L, Yang J A et al. A review of multi-sensor fusion SLAM systems based on 3D LIDAR[J]. Remote Sensing, 14, 2835(2022).

[2] Xu C, Zhou Y J, Luo C. Visual SLAM method based on optical flow and instance segmentation for dynamic scenes[J]. Acta Optica Sinica, 42, 1415002(2022).

[3] Shi X S, Li D J, Zhao P P et al. Are we ready for service robots? the OpenLORIS-scene datasets for lifelong SLAM[C], 3139-3145(2020).

[4] Shan T X, Englot B, Meyers D et al. LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping[C], 5135-5142(2021).

[5] Hess W, Kohler D, Rapp H et al. Real-time loop closure in 2D LIDAR SLAM[C], 1271-1278(2016).

[6] Kim G, Choi S, Kim A. Scan context: structural place recognition robust to rotation and lateral variations in urban environments[J]. IEEE Transactions on Robotics, 38, 1856-1874(2022).

[7] Chang Y H, Chen N S, Rao L et al. Lidar point cloud descriptor with rotation and translation invariance in dynamic environment[J]. Acta Optica Sinica, 42, 2401007(2022).

[8] Ding W D, Hou S H, Gao H et al. LiDAR inertial odometry aided robust LiDAR localization system in changing city scenes[C], 4322-4328(2020).

[9] Zhao M, Guo X, Song L et al. A general framework for lifelong localization and mapping in changing environment[C], 3305-3312(2021).

[10] Lu W X, Zhou Y, Wan G W et al. L3-net: towards learning based LiDAR localization for autonomous driving[C], 6382-6391(2020).

[11] Wiesmann L, Marcuzzi R, Stachniss C et al. Retriever: point cloud retrieval in compressed 3D maps[C], 10925-10932(2022).

[12] Luo L, Cao S Y, Han B et al. BVMatch: lidar-based place recognition using bird’s-eye view images[J]. IEEE Robotics and Automation Letters, 6, 6076-6083(2021).

[13] Vidanapathirana K, Ramezani M, Moghadam P et al. LoGG3D-net: locally guided global descriptor learning for 3D place recognition[C], 2215-2221(2022).

[14] Ren L J, Liu Y S, Zhong K D. Building method of semantic map based on improved PFPN[J]. Laser & Optoelectronics Progress, 59, 0428002(2022).

[15] Jiang L, Liu Q, Lei B et al. Fusion of laser and vision to identify and construct semantic map to improve positioning performance[J]. Chinese Journal of Lasers, 49, 1810003(2022).

[16] Wang Y, Chen H Y, Liu Y F et al. Edge-based monocular thermal-inertial odometry in visually degraded environments[EB/OL]. https://arxiv.org/abs/2210.10033

[17] Kazerouni I A, Fitzgerald L, Dooly G et al. A survey of state-of-the-art on visual SLAM[J]. Expert Systems With Applications, 205, 117734(2022).

[18] Javanmardi E, Javanmardi M, Gu Y L et al. Pre-estimating self-localization error of NDT-based map-matching from map only[J]. IEEE Transactions on Intelligent Transportation Systems, 22, 7652-7666(2021).

[19] Akai N, Morales L Y, Takeuchi E et al. Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching[C], 1356-1363(2017).

[20] Zhong K D, Liu Y S, Yang J S et al. A factor graph optimization mapping based on normaldistributions transform[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 30, 1127-1141(2022).

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

[22] Forster C, Carlone L, Dellaert F et al. On-manifold preintegration for real-time visual: inertial odometry[J]. IEEE Transactions on Robotics, 33, 1-21(2017).

[23] Kim G, Park Y S, Cho Y et al. MulRan: multimodal range dataset for urban place recognition[C], 6246-6253(2020).