{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T23:11:48Z","timestamp":1776985908350,"version":"3.51.4"},"reference-count":50,"publisher":"Cambridge University Press (CUP)","issue":"10","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"unspecified","delay-in-days":29,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, unmanned aerial vehicles (UAVs) have been applied in underground mine inspection and other similar works depending on their versatility and mobility. However, accurate localization of UAVs in perceptually degraded mines is full of challenges due to the harsh light conditions and similar roadway structures. Due to the unique characteristics of the underground mines, this paper proposes a semantic knowledge database-based localization method for UAVs. By minimizing the spatial point-to-edge distance and point-to-plane distance, the relative pose constraint factor between keyframes is designed for UAV continuous pose estimation. To reduce the accumulated localization errors during the long-distance flight in a perceptual-degraded mine, a semantic knowledge database is established by segmenting the intersection point cloud from the prior map of the mine. The topological feature of the current keyframe is detected in real time during the UAV flight. The intersection position constraint factor is constructed by comparing the similarity between the topological feature of the current keyframe and the intersections in the semantic knowledge database. Combining the relative pose constraint factor of LiDAR keyframes and the intersection position constraint factor, the optimization model of the UAV pose factor graph is established to estimate UAV flight pose and eliminate the cumulative error. Two UAV localization experiments conducted on the simulated large-scale Edgar Mine and a mine-like indoor corridor indicate that the proposed UAV localization method can realize accurate localization during long-distance flight in degraded mines.<\/jats:p>","DOI":"10.1017\/s0263574724001474","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T09:23:10Z","timestamp":1730280190000},"page":"3480-3504","source":"Crossref","is-referenced-by-count":3,"title":["A semantic knowledge database-based localization method\u00a0for UAV inspection in perceptual-degraded underground mine"],"prefix":"10.1017","volume":"42","author":[{"given":"Qinghua","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5585-6787","authenticated-orcid":false,"given":"Min","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"S0263574724001474_ref16","doi-asserted-by":"crossref","unstructured":"[16] Khattak, S. , Mascarich, F. , Dang, T. , Papachristos, C. and Alexis, K. . \u201cRobust thermal-inertial localization for aerial robots: A case for direct methods.\u201d 2019 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE (2019) pp. 1061\u20131068.","DOI":"10.1109\/ICUAS.2019.8798279"},{"key":"S0263574724001474_ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2018.2853729"},{"key":"S0263574724001474_ref29","doi-asserted-by":"crossref","unstructured":"[29] Hess, W. , Kohler, D. , Rapp, H. and Andor, D. . \" Real-Time Loop Closure in 2D Lidar Slam.\u201d 2016 IEEE international conference on robotics and automation (ICRA), IEEE (2016) pp. 1271\u20131278.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"S0263574724001474_ref33","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21978"},{"key":"S0263574724001474_ref19","first-page":"1","article-title":"Comparing lidar and imu-based slam approaches for 3D robotic mapping","volume":"41","author":"Fasiolo","year":"2023","journal-title":"Robotica"},{"key":"S0263574724001474_ref38","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574723001868"},{"key":"S0263574724001474_ref26","unstructured":"[26] Chow, J. F. , Kocer, B. B. , Henawy, J. , Seet, G. , Li, Z. , Yau, W. Y. and Pratama, M. , \u201cToward underground localization: Lidar inertial odometry enabled aerial robot navigation.\u201d (2019) [J]. CoRR abs\/1910.13085."},{"key":"S0263574724001474_ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2017.2705103"},{"key":"S0263574724001474_ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2021.3135234"},{"key":"S0263574724001474_ref31","doi-asserted-by":"crossref","unstructured":"[31] Wang, G. , Wu, X. , Liu, Z. and Wang, H. . \u201cPwclo-net: Deep Lidar Odometry in 3D Point Clouds using Hierarchical Embedding Mask Optimization.\u201d Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, (2021) pp. 15910\u201315919.","DOI":"10.1109\/CVPR46437.2021.01565"},{"key":"S0263574724001474_ref44","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574721000862"},{"key":"S0263574724001474_ref18","doi-asserted-by":"crossref","unstructured":"[18] Khattak, S. , Papachristos, C. and Alexis, K. . \u201cKeyframe-Based Direct Thermal\u2013Inertial Odometry.\u201d In 2019 International Conference on Robotics and Automation (ICRA), IEEE (2019) pp. 3563\u20133569.","DOI":"10.1109\/ICRA.2019.8793927"},{"key":"S0263574724001474_ref20","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20213"},{"key":"S0263574724001474_ref32","doi-asserted-by":"crossref","unstructured":"[32] Alexis, K. . \u201cResilient Autonomous Exploration and Mapping of Underground Mines using Aerial Robots.\u201d In 2019 19th International Conference on Advanced Robotics (ICAR), IEEE (2019) pp. 1\u20138.","DOI":"10.1109\/ICAR46387.2019.8981545"},{"key":"S0263574724001474_ref6","doi-asserted-by":"crossref","unstructured":"[6] \u00d6zaslan, T. , Mohta, K. , Keller, J. , Mulgaonkar, Y. , Taylor, C. J. , Kumar, V. , Wozencraft, J. M. and Hood, T. . \u201cTowards Fully Autonomous Visual Inspection of Dark Featureless Dam Penstocks using Mavs.\u201d In 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2016) pp. 4998\u20135005.","DOI":"10.1109\/IROS.2016.7759734"},{"key":"S0263574724001474_ref36","doi-asserted-by":"publisher","DOI":"10.1002\/rob.22152"},{"key":"S0263574724001474_ref41","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3092274"},{"key":"S0263574724001474_ref49","doi-asserted-by":"crossref","unstructured":"[49] Zhang, Z. and Scaramuzza, D. . \u201cA Tutorial on Quantitative Trajectory Evaluation for Visual (-Inertial) Odometry.\u201d In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2018) pp. 7244\u20137251.","DOI":"10.1109\/IROS.2018.8593941"},{"key":"S0263574724001474_ref27","doi-asserted-by":"crossref","unstructured":"[27] Kohlbrecher, S. , Von Stryk, O. , Meyer, J. and Klingauf, U. . \u201cA flexible and scalable slam system with full 3d motion estimation.\u201d In 2011 IEEE international symposium on safety, security, and rescue robotics, IEEE (2011) pp. 155\u2013160.","DOI":"10.1109\/SSRR.2011.6106777"},{"key":"S0263574724001474_ref25","doi-asserted-by":"crossref","unstructured":"[25] Papachristos, C. , Khattak, S. , Mascarich, F. and Alexis, K. . \u201cAutonomous navigation and mapping in underground mines using aerial robots.\u201d In 2019 IEEE Aerospace Conference, IEEE (2019) pp. 1\u20138.","DOI":"10.1109\/AERO.2019.8741532"},{"key":"S0263574724001474_ref22","first-page":"1","volume-title":"Robotics: Science and Systems","volume":"2","author":"Zhang","year":"2014"},{"key":"S0263574724001474_ref34","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21415"},{"key":"S0263574724001474_ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07488-7_9"},{"key":"S0263574724001474_ref10","article-title":"SNI-SLAM: Semantic \u00a0\u00a0\u00a0\u00a0,Neural Implicit SLAM\u201d, 2024","author":"Zhu","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition(CVPR)"},{"key":"S0263574724001474_ref12","doi-asserted-by":"publisher","DOI":"10.1177\/0278364914554813"},{"key":"S0263574724001474_ref23","doi-asserted-by":"crossref","unstructured":"[23] Shan, T. and Englot, B. . \u201cLego-Loam: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain.\u201d In. 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2018) pp. 4758\u20134765.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"S0263574724001474_ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3075644"},{"key":"S0263574724001474_ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2889304"},{"key":"S0263574724001474_ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2004.1371614"},{"key":"S0263574724001474_ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-9460-1_12"},{"key":"S0263574724001474_ref13","doi-asserted-by":"crossref","unstructured":"[13] Chen, L. J. , Henawy, J. , Kocer, B. B. and Seet, G. G. L. . \u201cAerial Robots on the Way to Underground: An Experimental Evaluation of Vins-Mono on Visual-Inertial Odometry Camera.\u201d In 2019 International Conference on Data Mining Workshops (ICDMW), IEEE (2019) pp. 91\u201396.","DOI":"10.1109\/ICDMW.2019.00024"},{"key":"S0263574724001474_ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3192102"},{"key":"S0263574724001474_ref50","doi-asserted-by":"crossref","unstructured":"[50] Wang, J. and Olson, E. . \u201cApriltag 2: Efficient and Robust Fiducial Detection.\u201d In 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2016) pp. 4193\u20134198.","DOI":"10.1109\/IROS.2016.7759617"},{"key":"S0263574724001474_ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rti.2005.03.006"},{"key":"S0263574724001474_ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2015.2463671"},{"key":"S0263574724001474_ref4","doi-asserted-by":"crossref","unstructured":"[4] Rogers, J. G. , Gregory, J. M. , Fink, J. and Stump, E. . Test your Slam! The Subt-Tunnel Dataset and Metric for Mapping. In 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE (2020) pp. 955\u2013961.","DOI":"10.1109\/ICRA40945.2020.9197156"},{"key":"S0263574724001474_ref40","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3208366"},{"key":"S0263574724001474_ref35","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.2976097"},{"key":"S0263574724001474_ref45","doi-asserted-by":"publisher","DOI":"10.1109\/18.910572"},{"key":"S0263574724001474_ref15","doi-asserted-by":"crossref","unstructured":"[15] Papachristos, C. , Mascarich, F. and Alexis, K. . \u201cThermal-inertial localization for autonomous navigation of aerial robots through obscurants.\u201d In 2018 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE (2018) pp. 394\u2013399.","DOI":"10.1109\/ICUAS.2018.8453447"},{"key":"S0263574724001474_ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2023.01.144"},{"key":"S0263574724001474_ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2017.2699790"},{"key":"S0263574724001474_ref28","doi-asserted-by":"crossref","unstructured":"[28] Grisetti, G. , Stachniss, C. and Burgard, W. . Improving Grid-Based Slam with Rao-Blackwellized particle Filters by Adaptive Proposals and Selective Resampling. In:\u00a0Proceedings of the 2005 IEEE international conference on robotics and automation, IEEE (2005) pp. 2432\u20132437.","DOI":"10.1109\/ROBOT.2005.1570477"},{"key":"S0263574724001474_ref37","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574724000511"},{"key":"S0263574724001474_ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-4939-4_3"},{"key":"S0263574724001474_ref46","doi-asserted-by":"publisher","DOI":"10.1145\/504729.504754"},{"key":"S0263574724001474_ref39","doi-asserted-by":"publisher","DOI":"10.1117\/1.OE.61.1.013106"},{"key":"S0263574724001474_ref9","doi-asserted-by":"crossref","unstructured":"[9] Jacobson, A. , Zeng, F. , Smith, D. , Boswell, N. , Peynot, T. and Milford, M. . \u201cSemi-Supervised Slam: Leveraging Low-Cost Sensors on Underground Autonomous Vehicles for Position Tracking.\u201d In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2018) pp. 3970\u20133977.","DOI":"10.1109\/IROS.2018.8593750"},{"key":"S0263574724001474_ref48","doi-asserted-by":"crossref","unstructured":"[48] Rogers, J. G. , Gregory, J. M. , Fink, J. and Stump, E. . \u201cTest your Slam! The Subt-Tunnel Dataset and Metric for Mapping.\u201d In 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE (2020) pp. 955\u2013961.","DOI":"10.1109\/ICRA40945.2020.9197156"},{"key":"S0263574724001474_ref17","doi-asserted-by":"crossref","unstructured":"[17] Khattak, S. , Papachristos, C. and Alexis, K. . \u201cVisual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments.\u201d In 2019 IEEE Aerospace Conference, IEEE (2019) pp. 1\u20139.","DOI":"10.1109\/AERO.2019.8741787"}],"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574724001474","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T05:33:22Z","timestamp":1737437602000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574724001474\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":50,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["S0263574724001474"],"URL":"https:\/\/doi.org\/10.1017\/s0263574724001474","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"value":"0263-5747","type":"print"},{"value":"1469-8668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10]]}}}