3D Reconstruction of Metro Tunnel Based on Path Likelihood Model and HMM Sequence Matching Localization

HU Zhaozheng; WANG Shuheng; MENG Jie; FENG Feng; ZHU Ziwei; LI Weigang

doi:10.11999/JEIT241122

Volume 47 Issue 7

Jul. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(7): 2273-2284

HU Zhaozheng, WANG Shuheng, MENG Jie, FENG Feng, ZHU Ziwei, LI Weigang. 3D Reconstruction of Metro Tunnel Based on Path Likelihood Model and HMM Sequence Matching Localization[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2273-2284. doi: 10.11999/JEIT241122

Citation:

HU Zhaozheng, WANG Shuheng, MENG Jie, FENG Feng, ZHU Ziwei, LI Weigang. 3D Reconstruction of Metro Tunnel Based on Path Likelihood Model and HMM Sequence Matching Localization[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2273-2284. doi: 10.11999/JEIT241122

Citation:

HU Zhaozheng, WANG Shuheng, MENG Jie, FENG Feng, ZHU Ziwei, LI Weigang. 3D Reconstruction of Metro Tunnel Based on Path Likelihood Model and HMM Sequence Matching Localization[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2273-2284. doi: 10.11999/JEIT241122

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3D Reconstruction of Metro Tunnel Based on Path Likelihood Model and HMM Sequence Matching Localization

doi: 10.11999/JEIT241122 cstr: 32379.14.JEIT241122

HU Zhaozheng^{1, 4
,
,},
WANG Shuheng^{1, 2},
MENG Jie¹,
FENG Feng¹,
ZHU Ziwei³,
LI Weigang⁴

1.
Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan 430070, China
2.
School of Information Engineering, Wuhan University of Technology, Wuhan 430063, China
3.
China Construction third Bureau Information Technology Co., LTD., Wuhan 430074, China
4.
Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China

Funds: The State Key Program of National Natural Science Foundation of China (52332010), China Construction Third Bureau Horizontal Projects (20233h0392), The Open Project of Engineering Research Center for Metallurgical Automation and Measurement Technology of MOE, Wuhan University of Science and Technology

Received Date: 2024-12-23
Rev Recd Date: 2025-04-01

Available Online: 2025-04-21

Publish Date: 2025-07-22

Abstract

Abstract

Objective As the operational mileage of metro systems in China continues to increase, the inspection and maintenance of metro tunnels have become more critical. Accurate 3D reconstruction of metro tunnels is essential for construction, inspection, and maintenance. However, in severely degraded tunnel environments, existing SLAM algorithms based on laser or vision often struggle to construct maps and face limitations in complex scenarios. To address this challenge, this paper proposes a method for large-scale 3D reconstruction of metro tunnels by utilizing the matching of the Path Likelihood Model (PLM) and the Hidden Markov Model (HMM). The 3D reconstruction task is divided into two key processes: odometer positioning and high-precision 3D reconstruction via graph optimization. High-precision 3D reconstruction is achieved by effectively addressing both components. Methods For odometer-based localization, this paper presents a method that incorporates the PLM. The PLM is developed using kernel density estimation to analyze the vehicle’s track path, effectively representing the vehicle’s positional information as a probability distribution. Within the framework of a particle filter, this method converts the constructed PLM into position observations of the vehicle. Additionally, data from the onboard Inertial Measurement Unit (IMU) and the wheel speed sensor are integrated to enhance localization accuracy. To minimize cumulative errors in odometer-based localization, this paper reformulates the problem of loop closure detection as a sequence matching problem using the Viterbi algorithm within the framework of the HMM. This method effectively addresses the instability associated with single-frame matching in loop closure detection and significantly improves the overall performance. To resolve the reconstruction problem, this paper presents a method for 3D reconstruction using large-scale factor graph optimization. By optimizing the pose graph with multiple constraints, it enables high-precision 3D reconstruction of extensive metro tunnels. Results and Discussions The proposed method and model are tested and validated at the WeiJianian-ShuangShuianian and ShaHeyuan-DongZikou metro stations in Chengdu. The experimental results are as follows: the effectiveness of the proposed method is confirmed through two sets of ablation experiments, DR and DR+PATH. Furthermore, by comparing the results with those of two notable open-source LIDAR algorithms, LIO-SAM and Faster-LIO, the superiority of this method is demonstrated. The reconstruction accuracy achieved is high, and the reconstruction error remains consistent even as the running distance increases. Therefore, the method is suitable for application in real operational processes. Conclusions This paper addresses the challenges of 3D reconstruction in metro tunnels by proposing a novel algorithm that combines the PLM with HMM sequence matching. The PLM is developed using drawing information, which serves as the foundation for the reconstruction process. Within the framework of particle filtering, the likelihood model is used to correct errors from the IMU and wheel speed sensor. This results in accurate odometer readings for the onboard robot. Furthermore, the issue of loop matching is reformulated as an HMM sequence matching problem. By constructing loop constraints, accumulated positioning errors are effectively eliminated. Finally, the pose and loop constraints derived from the odometer data are integrated into the optimization model for a large-scale factor map, enabling high-precision 3D reconstruction of the metro tunnel. Field tests conducted at the WeiJianian-ShuangShuianian and ShaHeyuan-DongZikou metro stations in Chengdu, with comparisons with other algorithms, demonstrate that the proposed PLM and HMM sequence matching algorithm significantly improve 3D reconstruction accuracy in metro tunnels, particularly in severely degraded environments.
- Metro tunnels,
- Degenerate scenarios,
- Path likelihood,
- Sequence matching,
- Factor graph optimization

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References(21)

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