Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (7): 49-58.DOI: 10.12068/j.issn.1005-3026.2025.20240212

• Industrial Intelligent Theory and Methods • Previous Articles     Next Articles

Research on Localization of Industrial Intelligent Inspection Robots in Cable Tunnel Environment

Yu-tao WANG1,2(), Jun-wei AN1, Chang-sheng QIN1, Wei-fan GUO1   

  1. 1.School of Information Science & Engineering,Northeastern University,Shenyang 110819,China
    2.Engineering Research Center of Digital Instrument for Process Industries,Ministry of Education,Northeastern University,Shenyang 110819,China.
  • Received:2024-11-10 Online:2025-07-15 Published:2025-09-24
  • Contact: Yu-tao WANG

Abstract:

The cable tunnel is closed and narrow, with repetitively laid cable racks and similar scene textures, which is a degraded scenario. To address this environment, a visual-inertial SLAM (simultaneous localization and mapping) algorithm based on point-line feature fusion is proposed. The algorithm improves the high-dimensional line features through length suppression and short line fitting to make it more effective in describing the structural features of tunnel scene. In addition, for the problem of loop closure detection failure due to feature similarity in cable tunnels, ArUco markers with efficient recognition and accurate pose estimation are introduced to limit the loop closure area, and the optimal loop closure frames are selected using the minimized pose transformation to improve detection accuracy and localization precision. Finally, dataset collection and experimental validation were conducted in actual cable tunnels. The results show that the absolute trajectory accuracy of the algorithm is improved by 69.73% on average relative to VINSMono(visual intertial system-Mono), which meets the application requirements of cable tunnel inspection.

Key words: simultaneous localization and mapping, point-line feature fusion, loop closure detection, industrial robot, cable tunnel

CLC Number: