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

doi:10.12068/j.issn.1005-3026.2025.20240212

Abstract

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 VINS–Mono（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:

TP 242

Yu-tao WANG, Jun-wei AN, Chang-sheng QIN, Wei-fan GUO. Research on Localization of Industrial Intelligent Inspection Robots in Cable Tunnel Environment[J]. Journal of Northeastern University(Natural Science), 2025, 46(7): 49-58.

Figures/Tables 16

Fig.1 Overall framework of positioning algorithm

Fig.2 Effectiveness of ORB feature point extraction in cable tunnel environment

Fig.3 Effectiveness of mismatch exclusion based on RANSAC algorithm

Fig.4 Flowchart of ILSD algorithm

Fig.5 Wrong fitting of line segment

Fig.6 Improved fitting result of line segment

Fig.7 Line feature matching based on LBD descriptor

Table 1 Line feature tracking time consumption comparison

提取算法	线段总数/条	提取时耗/ms	描述时耗/ms	匹配时耗/ms	总时耗/ms
LSD算法	784	94.855	73.476	86.543	254.874
ILSD算法	150	36.238	28.172	4.751	69.159

Fig.8 Wrong loop closure detection results

Fig.9 ArUco marker pattern

Fig.10 ArUco marker multi-frame loop closure detection

Fig.11 Filtering strategy of loop closure matching frame pair ensemble

Fig.12 ArUco marker pose measurement model

Fig.13 Schematic of robot kinematic coordinates

Table 2 Comparison of evaluation metrics under straight and curve datasets

评价指标	直道数据集			弯道数据集
评价指标	d	$d a v g$	$d m i d$	d	$d a v g$	$d m i d$
VINS-Mono/no loop	0.572	0.234	0.225	1.095	0.718	0.745
Ours-SLAM/no loop	0.353	0.143	0.140	0.862	0.454	0.493
Ours-SLAM /loop	0.222	0.138	0.132	0.238	0.130	0.131

Table 2 Comparison of evaluation metrics under straight and curve datasets

评价指标	直道数据集			弯道数据集
评价指标	d	$d a v g$	$d m i d$	d	$d a v g$	$d m i d$
VINS-Mono/no loop	0.572	0.234	0.225	1.095	0.718	0.745
Ours-SLAM/no loop	0.353	0.143	0.140	0.862	0.454	0.493
Ours-SLAM /loop	0.222	0.138	0.132	0.238	0.130	0.131

Fig.14 Trajectory results comparison

References 21

[1]	Mur-Artal R， Montiel J M M， Tardós J D. ORB-SLAM： a versatile and accurate monocular SLAM system［J］. IEEE Transactions on Robotics， 2015， 31（5）： 1147-1163.
[2]	Shan T X， Englot B. LeGO-LOAM： lightweight and ground-optimized lidar odometry and mapping on variable terrain［C］//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. Madrid： IEEE， 2018： 4758-4765.
[3]	Mourikis A I， Roumeliotis S I. A multi-state constraint Kalman filter for vision-aided inertial navigation［C］//Proceedings 2007 IEEE International Conference on Robotics and Automation. Rome： IEEE， 2007： 3565-3572.
[4]	Qin T， Li P L， Shen S J. VINS-Mono： a robust and versatile monocular visual-inertial state estimator［J］. IEEE Transactions on Robotics， 2018， 34（4）： 1004-1020.
[5]	Dang T， Mascarich F， Khattak S， et al. Autonomous search for underground mine rescue using aerial robots［C］//2020 IEEE Aerospace Conference. Big Sky： IEEE， 2020： 1-8.
[6]	Azpurua H， Campos M F M， Macharet D G. Three-dimensional terrain aware autonomous exploration for subterranean and confined spaces［C］//2021 IEEE International Conference on Robotics and Automation （ICRA）. Xi’an： IEEE， 2021： 2443-2449.
[7]	Campos C， Elvira R， Rodríguez J J G， et al. ORB-SLAM3： an accurate open-source library for visual， visual-inertial， and multimap SLAM［J］. IEEE Transactions on Robotics， 2021， 37（6）： 1874-1890.
[8]	Cao S Z， Lu X Y， Shen S J. GVINS： tightly coupled GNSS-visual-inertial fusion for smooth and consistent state estimation［J］. IEEE Transactions on Robotics， 2022， 38（4）： 2004-2021.
[9]	Tang H L， Niu X J， Zhang T S， et al. LE-VINS： a robust solid-state-LiDAR-enhanced visual-inertial navigation system for low-speed robots［J］. IEEE Transactions on Instrumentation and Measurement， 2023， 72： 8502113.
[10]	Song B Y， Yuan X F， Ying Z M， et al. DGM-VINS： visual-inertial SLAM for complex dynamic environments with joint geometry feature extraction and multiple object tracking［J］. IEEE Transactions on Instrumentation and Measurement， 2023， 72： 8503711.
[11]	Li M L， Zhang H， Shen T A， et al. SM-VINS： a fast and decoupled monocular visual-inertial sensors SLAM system with stepwise marginalization［J］. IEEE Sensors Journal， 2024， 24（20）： 33240-33251.
[12]	Zhou H Z， Zou D P， Pei L， et al. StructSLAM： visual SLAM with building structure lines［J］. IEEE Transactions on Vehicular Technology， 2015， 64（4）： 1364-1375.
[13]	Forster C， Pizzoli M， Scaramuzza D. SVO： fast semi-direct monocular visual odometry［C］//2014 IEEE International Conference on Robotics and Automation（ICRA）. Hong Kong： IEEE， 2014： 15-22.
[14]	Gomez-Ojeda R， Briales J， Gonzalez-Jimenez J. PL-SVO： semi-direct monocular visual odometry by combining points and line segments［C］//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. Daejeon： IEEE， 2016： 4211-4216.
[15]	Fu Q， Wang J， Yu H， et al. PL-VINS： real-time monocular visual-inertial SLAM with point and line features［J］. arXiv preprint arXiv， 2020： 2009.07462.
[16]	Lim H， Kim Y， Jung K， et al. Avoiding degeneracy for monocular visual SLAM with point and line features［C］//2021 IEEE International Conference on Robotics and Automation （ICRA）. Xi’an： IEEE， 2021： 11675-11681.
[17]	Zhu Y Q， Jin R， Lou T S， et al. PLD-VINS： RGBD visual-inertial SLAM with point and line features［J］. Aerospace Science and Technology， 2021， 119： 107185.
[18]	Lee J， Park S Y. PLF-VINS： real-time monocular visual-inertial SLAM with point-line fusion and parallel-line fusion［J］. IEEE Robotics and Automation Letters， 2021， 6（4）： 7033-7040.
[19]	Rublee E， Rabaud V， Konolige K， et al. ORB： an efficient alternative to SIFT or SURF ［C］//2011 IEEE International Conference on Computer Vision. Barcelona： IEEE， 2011： 2564-2571.
[20]	Von Gioi R G， Jakubowicz J， Morel J M， et al. LSD： a line segment detector［J］. Image Processing on Line， 2012， 2（4）： 35-55.
[21]	Hamid N， Khan N. LSM： perceptually accurate line segment merging［J］. Journal of Electronic Imaging， 2016， 25（6）： 061620.