Status and prospect of navigation and positioning in underground space

doi:10.13474/j.cnki.11-2246.2026.0902

Abstract

Abstract: The navigation and positioning in underground spaces have become one of the key areas for building a comprehensive positioning，navigation and timing(PNT)system.Focusing on underground space,the space classification based on navigation scenarios is conducted firstly,and the challenges faced by navigation and positioning are summarized systematically from seven aspects,including signal availability and scene structure.Secondly,the current main technologies and methods are sorted out in the aspects of multi-source sensor fusion,preset positioning base stations,prior matching,muon,and quantum and other technologies,and open-source multi-sensor datasets for underground space are also given.Finally,the future development trends are discussed in the aspects of front-end perception,sensors and platforms,cluster collaboration,and back-end optimization．

Key words: underground space, challenging scenario, navigation and positioning, multi-sensor fusion

CLC Number:

P228

Li Linyang, Zhao Dongqing, Zhou Qingyong, Lai Luguang, Yuan Junyan, Hui Jun, Fan Wenzhe, Li Pengfei. Status and prospect of navigation and positioning in underground space[J]. Bulletin of Surveying and Mapping, 2026, 0(9): 7-18.

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References

[1] 杨元喜, 杨诚, 任夏.PNT智能服务[J].测绘学报, 2021, 50(8):1006-1012.
[2] 刘经南, 罗亚荣, 郭迟, 等.PNT智能与智能PNT[J].测绘学报, 2022, 51(6):811-828.
[3] 杨元喜, 任夏, 贾小林, 等.以北斗系统为核心的国家安全PNT体系发展趋势[J].中国科学:地球科学, 2023, 53(5):917-927.
[4] 党亚民, 蒋涛, 杨元喜, 等.中国大地测量研究进展(2019—2023)[J].测绘学报, 2023, 52(9):1419-1436.
[5] 中国科学技术协会.中国科协发布2020重大科学技术问题和工程技术难题[EB/OL].(2020-08-16)[2025-01-01].https://www.cast.org.cn/xw/MTBD/art/2020/art_4f50d32013a64a20b58e7f3fcbe56984.html.
[6] 中华人民共和国中央人民政府.国务院关于印发新一代人工智能发展规划的通知[EB/OL].(2017-07-20)[2025-01-01].https://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[7] 中华人民共和国国防部.地下空间:未来战争的关键战场[EB/OL].(2018-10-18)[2025-01-01].http://www.mod.gov.cn/gfbw/jmsd/4826976.html.
[8] Kim H, Choi Y.Location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched LiDAR sequential images[J].International Journal of Mining Science and Technology, 2021, 31(5):779-788.
[9] Wang Junhui, Tian Bin, Zhang Rui, et al.ULSM:underground localization and semantic mapping with salient region loop closure under perceptually-degraded environment[C]//Proceedings of 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Kyoto:IEEE, 2022:1320-1327.
[10] 李新年, 李清华, 王常虹, 等.美国地下领域无人系统发展现状及启示[J].导航定位与授时, 2021, 8(6):52-59.
[11] Ebadi K, Bernreiter L, Biggie H, et al.Present and future of SLAM in extreme environments:the DARPA SubT challenge[J].IEEE Transactions on Robotics, 2024, 40:936-959.
[12] Tranzatto M, Miki T, Dharmadhikari M, et al.CERBERUS in the DARPA subterranean challenge[J].Science Robotics, 2022, 7(66):eabp9742.
[13] Petrlík M, Báča T, Heǐt D, et al.A robust UAV system for operations in a constrained environment[J].IEEE Robotics and Automation Letters, 2020, 5(2):2169-2176.
[14] Rogers J G, Gregory J M, Fink J, et al.Test your SLAM！ The SubT-tunnel dataset and metric for mapping[C]//Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA).Paris:IEEE, 2020:955-961.
[15] Lee J, Komatsu R, Shinozaki M, et al.Switch-SLAM:switching-based LiDAR-inertial-visual SLAM for degenerate environments[J].IEEE Robotics and Automation Letters, 2024, 9(8):7270-7277.
[16] Azpúrua H, Rezende A, Potje G, et al.Towards semi-autonomous robotic inspection and mapping in confined spaces with the EspeleoRobô[J].Journal of Intelligent &Robotic Systems, 2021, 101(4):69.
[17] Khattak S, Mascarich F, Dang T, et al.Robust thermal-inertial localization for aerial robots:a case for direct methods[C]//Proceedings of 2019 International Conference on Unmanned Aircraft Systems (ICUAS).Atlanta:IEEE, 2019:1061-1068.
[18] Khattak S, Nguyen H, Mascarich F, et al.Complementary multi-modal sensor fusion for resilient robot pose estimation in subterranean environments[C]//Proceedings of 2020 International Conference on Unmanned Aircraft Systems (ICUAS).Athens:IEEE, 2020:1024-1029.
[19] Tian Yulun, Chang Yun, Herrera Arias F, et al.Kimera-multi:robust, distributed, dense metric-semantic SLAM for multi-robot systems[J].IEEE Transactions on Robotics, 2022, 38(4):2022-2038.
[20] Zhong Shipeng, Qi Yuhua, Chen Zhiqiang, et al.DCL-SLAM:a distributed collaborative LiDAR SLAM framework for a robotic swarm[J].IEEE Sensors Journal, 2024, 24(4):4786-4797.
[21] Palieri M, Morrell B, Thakur A, et al.LOCUS:a multi-sensor LiDAR-centric solution for high-precision odometry and 3D mapping in real-time[J].IEEE Robotics and Automation Letters, 2021, 6(2):421-428.
[22] Feng Shaoquan, Li Xingxing, Yan Zhuohao, et al.Tightly coupled integration of LiDAR and vision for 3D multiobject tracking[J].IEEE Transactions on Intelligent Vehicles, 2025, 10(1):399-412.
[23] Kong Da, Zhang Yu, Dai Weichen.Direct near-infrared-depth visual SLAM with active lighting[J].IEEE Robotics and Automation Letters, 2021, 6(4):7057-7064.
[24] Wang Hao, Sun Qian, Zou Junjing, et al.Visual-inertial SLAM algorithm for low-texture subterranean environments[C]//Proceedings of 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT).Qingdao:IEEE, 2023:1-3.
[25] Sun Qian, Wang Hao, Liu Wa, et al.An improved stereo visual-inertial SLAM algorithm based on point-and-line features for subterranean environments[J].IEEE Transactions on Vehicular Technology, 2025, 74(3):3925-3940.
[26] Rho S, Park S M, Pyo J, et al.LiDAR-stereo thermal sensor fusion for indoor disaster environment[J].IEEE Sensors Journal, 2023, 23(7):7816-7827.
[27] 赖路广, 赵冬青, 李林阳, 等.长波红外成像技术及其在SLAM中的应用[J].测绘科学, 2024, 49(3):77-86.
[28] Vidas S, Lakemond R, Denman S, et al.An exploration of feature detector performance in the thermal-infrared modality[C]//Proceedings of 2011 International Conference on Digital Image Computing:Techniques and Applications.Noosa:IEEE, 2011:217-224.
[29] Ricaurte P, Chilán C, Aguilera-Carrasco C A, et al.Feature point descriptors:infrared and visible spectra[J].Sensors, 2014, 14(2):3690-3701.
[30] Johansson J, Solli M, Maki A.An evaluation of local feature detectors and descriptors for infrared images[C]//Proceedings of 2016 Computer Vision.Cham:Springer International Publishing, 2016:711-723.
[31] Mouats T, Aouf N, Nam D, et al.Performance evaluation of feature detectors and descriptors beyond the visible[J].Journal of Intelligent &Robotic Systems, 2018, 92(1):33-63.
[32] Van Manen B R, Sluiter V, Mersha A Y.FirebotSLAM:thermal SLAM to increase situational awareness in smoke-filled environments[J].Sensors, 2023, 23(17):7611.
[33] Lu Yawen, Lu Guoyu.SuperThermal:matching thermal as visible through thermal feature exploration[J].IEEE Robotics and Automation Letters, 2021, 6(2):2690-2697.
[34] Emilsson E, Rydell J.Chameleon on fire:thermal infrared indoor positioning[C]//Proceedings of 2014 IEEE/ION Position, Location and Navigation Symposium.Monterey:IEEE, 2014:637-644.
[35] Delaune J, Hewitt R, Lytle L, et al.Thermal-inertial odometry for autonomous flight throughout the night[C]//Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Macao:IEEE, 2019:1122-1128.
[36] He Mengwen, Rajkumar R R.Using thermal vision for extended VINS-mono to localize vehicles in large-scale outdoor road environments[C]//Proceedings of 2021 IEEE Intelligent Vehicles Symposium (IV).Nagoya:IEEE, 2021:953-960.
[37] Khattak S, Papachristos C, Alexis K.Keyframe-based thermal-inertial odometry[J].Journal of Field Robotics, 2020, 37(4):552-579.
[38] Saputra M R U, De Gusmao P P B, Lu C X, et al.DeepTIO:a deep thermal-inertial odometry with visual hallucination[J].IEEE Robotics and Automation Letters, 2020, 5(2):1672-1679.
[39] Zhao Shibo, Wang Peng, Zhang Hengrui, et al.TP-TIO:a robust thermal-inertial odometry with deep ThermalPoint[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Las Vegas:IEEE, 2020:4505-4512.
[40] Kong Xianglong, Wu Wenqi, Zhang Lilian, et al.Tightly-coupled stereo visual-inertial navigation using point and line features[J].Sensors, 2015, 15(6):12816-12833.
[41] Chen Wenqiang, Wang Yu, Chen Haoyao, et al.EIL-SLAM:depth-enhanced edge-based infrared-LiDAR SLAM[J].Journal of Field Robotics, 2022, 39(2):117-130.
[42] Wang Yu, Chen Haoyao, Liu Yufeng, et al.Edge-based monocular thermal-inertial odometry in visually degraded environments[J].IEEE Robotics and Automation Letters, 2023, 8(4):2078-2085.
[43] Pan Huimin, Liu Dongfeng, Ren Jingzheng, et al.LiDAR-IMU tightly-coupled SLAM method based on IEKF and loop closure detection[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17:6986-7001.
[44] Zhang Ji, Singh S.LOAM:LiDAR odometry and mapping in real-time[C]//Proceedings of 2014 Robotics:Science and Systems X.Berkeley:[s.n.], 2014.
[45] Shan Tixiao, Englot B.LeGO-LOAM:lightweight and ground-optimized LiDAR odometry and mapping on variable terrain[C]//Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Madrid:IEEE, 2018:4758-4765.
[46] Shan Tixiao, Englot B, Meyers D, et al.LIO-SAM:tightly-coupled LiDAR inertial odometry via smoothing and mapping[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Las Vegas:IEEE, 2020:5135-5142.
[47] Hewitt R A, Marshall J A.Towards intensity-augmented SLAM with LiDAR and ToF sensors[C]//Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Hamburg:IEEE, 2015:1956-1961.
[48] Zhou Baoding, Xie Doudou, Chen Shoubin, et al.Comparative analysis of SLAM algorithms for mechanical LiDAR and solid-state LiDAR[J].IEEE Sensors Journal, 2023, 23(5):5325-5338.
[49] Lin Jiarong, Zhang Fu.Loam livox:a fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV[C]//Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA).Paris:IEEE, 2020:3126-3131.
[50] Xu Wei, Zhang Fu.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.
[51] Xu Wei, Cai Yixi, He Dongjiao, et al.FAST-LIO2:fast direct LiDAR-inertial odometry[J].IEEE Transactions on Robotics, 2022, 38(4):2053-2073.
[52] Bai Chunge, Xiao Tao, Chen Yajie, et al.Faster-LIO:lightweight tightly coupled LiDAR-inertial odometry using parallel sparse incremental voxels[J].IEEE Robotics and Automation Letters, 2022, 7(2):4861-4868.
[53] Chen Jiahui, Zi Yindong, Li Nian, et al.VPE-SLAM:virtual point enhanced SLAM using solid-state LiDAR for weak feature environments[J].IEEE Sensors Journal, 2024, 24(10):16397-16407.
[54] Zhou Lipu, Huang Guoquan, Mao Yinian, et al.LiSLAM:LiDAR SLAM with planes, lines, and cylinders[J].IEEE Robotics and Automation Letters, 2022, 7(3):7163-7170.
[55] Guo Wenzhuo, Li Linyang, Zhao Dongqing, et al.Enhancing BDS-3 PPP-B2b real-time positioning performance by tightly integrating MEMS IMU and LiDAR in GNSS-degraded environment[J].Measurement, 2025, 242:116015.
[56] Dong Yanchao, Li Lingxiao, Liu Yuhao, et al.PVE-LIOM:pseudo-visual enhanced LiDAR-inertial odometry and mapping[J].IEEE Transactions on Instrumentation and Measurement, 2023, 72:8506013.
[57] Zhang Yanfeng, Tian Yunong, Wang Wanguo, et al.RI-LIO:reflectivity image assisted tightly-coupled LiDAR-inertial odometry[J].IEEE Robotics and Automation Letters, 2023, 8(3):1802-1809.
[58] Du Wenqiang, Beltrame G.Real-time simultaneous localization and mapping with LiDAR intensity[C]//Proceedings of 2023 IEEE International Conference on Robotics and Automation (ICRA).London:IEEE, 2023:4164-4170.
[59] Dong Yanchao, Li Lingxiao, Xu Sixiong, et al.R-LIOM:reflectivity-aware LiDAR-inertial odometry and mapping[J].IEEE Robotics and Automation Letters, 2023, 8(11):7743-7750.
[60] Li Haisong, Tian Bailing, Shen Hongming, et al.An intensity-augmented LiDAR-inertial SLAM for solid-state LiDARs in degenerated environments[J].IEEE Transactions on Instrumentation and Measurement, 2022, 71:8503610.
[61] Shin Y S, Kim A.Sparse depth enhanced direct thermal-infrared SLAM beyond the visible spectrum[J].IEEE Robotics and Automation Letters, 2019, 4(3):2918-2925.
[62] 白师宇, 赖际舟, 吕品, 等.面向地下空间探测的移动机器人定位与感知方法[J].机器人, 2022, 44(4):463-470.
[63] 蔡毅, 王岭雪.红外成像技术中的9个问题[J].红外技术, 2013, 35(11):671-682.
[64] Wen Tianci, Fang Yongchun, Lu Biao, et al.LIVER:a tightly coupled LiDAR-inertial-visual state estimator with high robustness for underground environments[J].IEEE Robotics and Automation Letters, 2024, 9(3):2399-2406.
[65] Leung K, Lühr D, Houshiar H, et al.Chilean underground mine dataset[J].The International Journal of Robotics Research, 2017, 36(1):16-23.
[66] Wen Weisong, Zhou Yiyang, Zhang Guohao, et al.UrbanLoco:a full sensor suite dataset for mapping and localization in urban scenes[C]//Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA).Paris:IEEE, 2020:2310-2316.
[67] Helmberger M, Morin K, Berner B, et al.The hilti SLAM challenge dataset[J].IEEE Robotics and Automation Letters, 2022, 7(3):7518-7525.
[68] Nair A D, Kindle J, Levchev P, et al.Hilti SLAM challenge 2023:benchmarking single multi-session SLAM across sensor constellations in construction[J].IEEE Robotics and Automation Letters, 2024, 9(8):7286-7293.
[69] Li Qingqing, Yu Xianjia, Queralta J P, et al.Multi-modal lidar dataset for benchmarking general-purpose localization and mapping algorithms[C]//Proceedings of 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Kyoto:IEEE, 2022:3837-3844.
[70] Li Jianping, Wu Weitong, Yang Bisheng, et al.WHU-helmet:a helmet-based multisensor SLAM dataset for the evaluation of real-time 3D mapping in large-scale GNSS-denied environments[J].IEEE Transactions on Geoscience and Remote Sensing, 2023, 61:5702016.
[71] Chen Zhiqiang, Qi Yuhua, Feng Dapeng, et al.Heterogeneous LiDAR dataset for benchmarking robust localization in diverse degenerate scenarios[EB/OL].(2024-09-10)[2024-10-31].https://doi.org/10.48550/arXiv.2409.04961.
[72] Zhao Shibo, Gao Yuanjun, Wu Tianhao, et al.SubT-MRS dataset:pushing SLAM towards all-weather environments[EB/OL].(2024-05-30)[2024-11-27].https://doi.org/10.48550/arXiv.2307.07607.
[73] Jia Yanpeng, Wang Shiyi, Shao Shiliang, et al.M2UD:a multi-model, multi-scenario, uneven-terrain dataset for ground robot with localization and mapping evaluation[EB/OL].(2025-03-16)[2025-04-20].https://doi.org/10.48550/arXiv.2503.12387.
[74] Gong Weisheng, He Chen, Su Kaijie, et al.DIDLM:a SLAM dataset for difficult scenarios featuring infrared, depth cameras, LiDAR, 4D radar, and others under adverse weather, low light conditions, and rough roads[J].IEEE Transactions on Intelligent Transportation Systems, 2026, 27(2):1908-1920.
[75] 陈锐志, 郭光毅, 陈亮, 等.室内高精度定位技术研究应用现状与发展趋势[J].武汉大学学报(信息科学版), 2023, 48(10):1591-1600.
[76] 周宁.地下空间UWB高精度定位关键技术研究[D].北京:中国测绘科学研究院, 2023.
[77] 孙猛, 汪云甲, 王潜心, 等.Wi-Fi RTT/RSS混合定位CRLB推导与最优节点布局设计[J].测绘学报, 2024, 53(10):1993-2006.
[78] 乔玉坤, 王仕成, 张琪.地磁匹配特征量的选择[J].地震地磁观测与研究, 2007, 28(1):42-47.
[79] 陆一, 魏东岩, 纪新春, 等.地磁定位方法综述[J].导航定位与授时, 2022, 9(2):118-130.
[80] Zhang Tisheng, Wei Linfu, Kuang Jian, et al.Mag-ODO:motion speed estimation for indoor robots based on dual magnetometers[J].Measurement, 2023, 222:113688.
[81] Vallivaara I, Haverinen J, Kemppainen A, et al.Magnetic field-based SLAM method for solving the localization problem in mobile robot floor-cleaning task[C]//Proceedings of the 15th International Conference on Advanced Robotics (ICAR).Tallinn:IEEE, 2011:198-203.
[82] Jung J, Oh T, Myung H.Magnetic field constraints and sequence-based matching for indoor pose graph SLAM[J].Robotics and Autonomous Systems, 2015, 70:92-105.
[83] 杨雨成, 任夏.基于宇宙射线的μ子导航[J].导航定位学报, 2023, 11(3):8-13.
[84] Tanaka H K M.Muometric positioning system (μPS)with cosmic muons as a new underwater and underground positioning technique[J].Scientific Reports, 2020, 10:18896.
[85] Tanaka H K M.Wireless muometric navigation system[J].Scientific Reports, 2022, 12:10114.
[86] Tanaka H K M, Gallo G, Gluyas J, et al.First navigation with wireless muometric navigation system (MuWNS)in indoor and underground environments[J].iScience, 2023, 26(7):107000.
[87] Varga D, Tanaka H K M.Developments of a centimeter-level precise muometric wireless navigation system (MuWNS-V)and its first demonstration using directional information from tracking detectors[J].Scientific Reports, 2024, 14:7605.
[88] 熊凯, 魏春岭, 周鹏.基于宇宙线缪子探测的水下定位方法研究[J].战术导弹技术, 2023(4):56-68.
[89] Li Haoyang, Zhang Chunxi, Fan Xingming, et al.A muon high-resolution pseudorange measurement method:application to muon navigation in confined spaces[J].Chinese Journal of Aeronautics, 2024, 37(4):391-404.
[90] Tanaka H K M, Bozza C, Bross A, et al.Muography[J].Nature Reviews Methods Primers, 2023, 3:88.
[91] 邓敏, 张燚, 钱天予, 等.基于原子体系的量子惯性传感器研究现状[J].仪器仪表学报, 2023, 44(9):14-38.
[92] 阮驰, 张昌昌, 尹飞, 等.光纤光子纠缠增强陀螺的现状与认知[J].导航与控制, 2021, 20(4):9-23.
[93] Yao Zhanwei, Lu Sibin, Li Runbing, et al.Continuous dynamic rotation measurements using a compact cold atom gyroscope[J].Chinese Physics Letters, 2016, 33(8):083701.
[94] 陈红辉, 姚战伟, 陆泽茜, 等.可搬运高精度原子干涉陀螺仪[J].导航与控制, 2022, 21(5):42-50.
[95] 陈颖, 刘占超, 刘刚.核磁共振陀螺仪研究进展[J].控制理论与应用, 2019, 36(7):1017-1023.
[96] Jiang Liwei, Quan Wei, Li Rujie, et al.A parametrically modulated dual-axis atomic spin gyroscope[J].Applied Physics Letters, 2018, 112(5):054103.
[97] Zhao Liye, Shen Xiang, Ji Lumin, et al.Inertial measurement with solid-state spins of nitrogen-vacancy center in diamond[J].Advances in Physics:X, 2022, 7(1):2004921.
[98] Wu Yuan, Guo Jinxian, Feng Xiaotian, et al.Atom-light hybrid quantum gyroscope[J].Physical Review Applied, 2020, 14(6):064023.