Acta Geodaetica et Cartographica Sinica ›› 2025, Vol. 54 ›› Issue (2): 297-307.doi: 10.11947/j.AGCS.2025.20240018

• Photogrammetry and Remote Sensing • Previous Articles    

3D tunnel mapping method combining registration compensation and spatial constraint

Xing ZHANG1,2,3,4(), Zhanpeng HUANG1,2,3,4, Qingquan LI2,3,4,5, Baoding ZHOU2,5(), Qipei LI1,2,3,4   

  1. 1.School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    2.Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China
    3.Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, MNR, Shenzhen University, Shenzhen 518060, China
    4.Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
    5.College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
  • Received:2024-01-12 Published:2025-03-11
  • Contact: Baoding ZHOU E-mail:xzhang@szu.edu.cn;bdzhou@szu.edu.cn
  • About author:ZHANG Xing (1982—), male, PhD, associate professor, majors in multi-sensor fusion positioning and 3D mapping. E-mail: xzhang@szu.edu.cn
  • Supported by:
    The National Key Research and Development Program of China(2022YFB3904602);The National Natural Science Foundation of China(42071434);Shenzhen Science and Technology Program(JCYJ20240813142833044);The Innovation Team of the Department of Education of Guangdong Province(2024KCXTD013);The Center for Scientific Research and Development in Higher Education Institutes, MOE(2024HT013)

Abstract:

Three-dimensional (3D) maps are crucial for early-warning construction safety and long-term safety maintenance of tunnels. However, generating an accurate 3D point cloud map in tunnels characterized by sparse textures, rough structures, and dynamic interference poses a challenging task. This paper proposes a 3D tunnel mapping method to generate point cloud maps of extremely long and noisy scenes. First, a registration residual compensation model is proposed to eliminate the registration errors caused by rough surface structures. The K-means clustering method is used to identify non-planar surface structures, and compensation is carried out based on local region residual. Then, a spatial constraint strategy based on view field maximization is proposed to eliminate point cloud errors caused by absolute measurement deviations. To verify the performance of the proposed method, we conducted experiments during the secondary lining and pipeline laying stages in both drilling and blasting and shield tunnels. The results indicate that the proposed method outperforms the methods of FAST-LIO2, Faster-LIO, and LiLi-OM in terms of both trajectory estimation and map accuracy. Additionally, ablation experiments were conducted to elucidate the contributions of different models in 3D mapping of tunnels.

Key words: mapping, registration compensation, spatial constraint, LiDAR, legged robot

CLC Number: