High precision localization for periodic tunnel inspections

References

• Ishikawa K, Takiguchi J, Amano Y, et al. Tunnel cross-section measurement system using a mobile mapping system. J Robot Mechatron. 2009;21(2):193–199. doi:10.20965/jrm.2009.p0193
   Google Scholar
• Satou A, Esaki R, Amano Y, et al. Tunnel location estimation for periodic tunnel inspections. In: JSME Conference on Robotics and mechatronics. 2020. Kanazawa (Japan): 2020. p. 1P2–L09.
   Google Scholar
• Odaka T, Ishikawa K. Localization in tunnels using feature-based scan matching. In: SICE Annual Conference September 6–9, 2023. Tsu (Japan): Mie University; 2023. p. 1284–1289.
   Google Scholar
• Sakuma Y, Ishikawa K, Yamazaki T, et al. Evaluation of a positioning correction method in GPS blockage condition. In: SICE Annual Conference September 13–18, 2011. Tokyo (Japan): Waseda University; 2011. p. 1670–1674.
   Google Scholar
• Zafari F, Gkelias A, Leung KK. A survey of indoor localization systems and technologies. IEEE Commun Surv Tutorials. 2019;21(3):2568–2599. doi:10.1109/COMST.2019.2911558
   Web of Science ®Google Scholar
• Gan X, Yu B, Wang X, et al. A new array pseudolites technology for high precision indoor positioning. IEEE Access. 2019;7:153269–153277. doi:10.1109/ACCESS.2019.2948034
   Web of Science ®Google Scholar
• Sun W, Xue M, Yu H, et al. Augmentation of fingerprints for indoor WiFi localization based on Gaussian process regression. IEEE Trans Veh Technol. 2018;67(11):10896–10905. doi:10.1109/TVT.2018.2870160
   Web of Science ®Google Scholar
• Jang B, Kim H. Indoor positioning technologies without offline fingerprinting map: a survey. IEEE Commun Surv Tutorials. 2019;21(1):508–525. doi:10.1109/COMST.2018.2867935
   Web of Science ®Google Scholar
• Mur-Artal R, Tardós JD. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans Robot. 2017;33(5):1255–1262. doi:10.1109/TRO.2017.2705103
   Web of Science ®Google Scholar
• Merzlyakov A, Macenski S. A comparison of modern general-purpose visual SLAM approaches. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic; 2021. p. 9190–9197.
   Google Scholar
• Shan T, Englot B. LeGO-LOAM: lightweight and ground-optimized LiDAR odometry and mapping on variable terrain. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain; 2018. p. 4758–4765.
   Google Scholar
• Shan T, Englot B, Meyers D, et al. LIO-SAM: tightly-coupled LiDAR inertial odometry via smoothing and mapping. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA; 2020. p. 5135–5142.
   Google Scholar
• Chan TH, Hesse H, Ho SG. LiDAR-based 3D SLAM for indoor mapping. 2021 7th International Conference on Control, Automation and Robotics (ICCAR), Singapore; 2021. p. 285–289.
   Google Scholar
• Wang L, Zhu Y, Liu H, et al. Research on mobile robot localization and mapping method for underground long-narrow tunnels. 2022 4th International Conference on Robotics and Computer Vision (ICRCV), Wuhan, China; 2022. p. 345–349.
   Google Scholar
• Besl PJ, McKay ND. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell. 1992;14(2):239–256. doi:10.1109/34.121791
   Web of Science ®Google Scholar
• Biber P, Strasser W. The normal distributions transform: a new approach to laser scan matching. Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), Las Vegas, NV, USA; 2003. Vol. 3. p. 2743–2748.
   Google Scholar
• Brookner E. Tracking and Kalman filtering made easy. 1st ed., Vol. 4. New York: Wiley-Interscience; 1998.
   Google Scholar
• Thrun S, Burgard W, Fox D. Probabilistic robotics. Cambridge (MA): Massachusetts Institute of Technology; 2006.
   Google Scholar
• Kitagawa G. A Monte Carlo filtering and smoothing method for non-Gaussian nonlinear state space models. Proceedings of the 2nd U.S.-Japan Joint Seminar on Statistical Time Series Analysis Honolulu, Hawaii, January 25–29; 1993.
   Google Scholar
• Doucet A. On sequential simulation-based methods for Bayesian filtering, Technical report CUED/F-INFENG/TR.310. 1998.
   Google Scholar
• Kalman RE. A new approach to linear filtering and prediction problems. J Basic Eng. 1960;82:35–45. doi:10.1115/1.3662552
   Google Scholar
• Julier SJ, Uhlmann JK. Unscented filtering and nonlinear estimation. Proc IEEE. 2004;92(3):401–422. doi:10.1109/JPROC.2003.823141
   Web of Science ®Google Scholar
• Jinno I, Sasaki Y, Mizoguchi H. 3D map update in human environment using change detection from LiDAR equipped mobile robot. 2019 IEEE/SICE International Symposium on System Integration (SII), Paris, France; 2019. p. 330–335.
   Google Scholar
• Chang Y-C, Chen Y-L, Hsu Y-W, et al. Integrating V-SLAM and LiDAR-based SLAM for map updating. 2021 IEEE 4th International Conference on Knowledge Innovation and Invention (ICKII), Taichung, Taiwan; 2021. p. 134–139.
   Google Scholar
• Chiang K-W, Tsai G-J, Li Y-H, et al. Development of LiDAR-based UAV system for environment reconstruction. IEEE Geosci Remote Sens Lett. 2017;14(10):1790–1794. doi:10.1109/LGRS.2017.2736013
   Web of Science ®Google Scholar
• Tang Y, Hu Y, Cui J, et al. Vision-aided multi-UAV autonomous flocking in GPS-denied environment. IEEE Trans Ind Electron. 2019;66(1):616–626. doi:10.1109/TIE.2018.2824766
   Web of Science ®Google Scholar
• Kim K, Im J, Jee G. Tunnel facility based vehicle localization in highway tunnel using 3D LiDAR. IEEE Trans Intell Transp Syst. 2022;23(10):17575–17583. doi:10.1109/TITS.2022.3160235
   Web of Science ®Google Scholar
• Mengtong L, Linwei T. Synchronization technology and system based on 1 pulse per second signal. 2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xi'an, China; 2021, p. 1–5.
   Google Scholar