Abstract
Various coal mine robots (CMRs) and unmanned aerial vehicles (UAVs) are implemented to explore unknown mines for improving the safety and efficiency of mining. It is challenging for CMRs and UAVs to achieve accurate positioning due to the absence of GPS, poor lighting conditions, and similar geometric features in complex mine scenes. LiDAR-based localization and mapping methods are admittedly more accurate than others, while long-time running in large-scale scenarios will also introduce non-negligible cumulative errors. This study presents a semantic-aided LiDAR simultaneous localization and mapping (SLAM) with loop closure, which leverages the uniqueness of mine intersection structure to establish stable semantic loop closure. Specifically, we propose a semantic intersection descriptor of translation and rotation invariance, which encodes 3D point clouds of the same intersection from different positions and viewpoints into a unified image. By using the semantic descriptor, we can construct a constant loop constraint when the same intersection is revisited from different directions to reduce cumulative drift. We provide experimental validation using large datasets collected in two large underground mines, namely, a simulated Edgar mine deployed in ROS Gazebo and a public underground mine dataset provided by ETH. Experimental results show that the proposed method has higher localization accuracy and outperforms the existing LiDAR-based SLAM strategies.
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References
Young A, Rogers P (2019) A review of digital transformation in mining. Mining, metallurgy & exploration 36(4):683–699
Rogers WP, Kahraman MM, Drews FA, Powell K, Haight JM, Wang Y, Baxla K, Sobalkar M (2019) Automation in the mining industry: review of technology, systems, human factors, and political risk. Mining, metallurgy & exploration 36(4):607–631
Wang W, Dong W, Su Y, Wu D, Du Z (2014) Development of search-and-rescue robots for underground coal mine applications. Journal of Field Robotics 31(3):386–407
Li M, Zhu H, You S, Wang L, Tang C (2018) Efficient laser-based 3D SLAM for coal mine rescue robots. IEEE Access 7:14124–14138
Kim H, Choi Y (2021) Location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched lidar sequential images. International Journal of mining science and technology 31(5):779–788
Mansouri SS, Kanellakis C, Kominiak D, Nikolakopoulos G (2020) Deploying MAVs for autonomous navigation in dark underground mine environments. Robotics and Autonomous Systems 126:103472
Petráček P, Krátkỳ V, Petrlík M, Báča T, Kratochvíl R, Saska M (2021) Large-scale exploration of cave environments by unmanned aerial vehicles. IEEE Robotics and Automation Letters 6(4):7596–7603
Tang M, Esmaeili K (2021) Mapping surface moisture of a gold heap leach pad at the El Gallo Mine using a UAV and thermal imaging. Mining, Metallurgy & Exploration 38(1):299–313
Kasper, M., McGuire, S., Heckman, C.: A benchmark for visual-inertial odometry systems employing onboard illumination. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5256–5263 (2019). IEEE
Papachristos, C., Khattak, S., Mascarich, F., Alexis, K.: Autonomous navigation and mapping in underground mines using aerial robots. In: 2019 IEEE Aerospace Conference, pp. 1–8 (2019). IEEE
Zhang, J., Singh, S.: LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems, vol. 2, pp. 1–9 (2014). Berkeley, CA
Shan, T., Englot, B.: LeGO-LOAM: lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4758–4765 (2018). IEEE
Tabib, W., Michael, N.: Simultaneous localization and mapping of subterranean voids with Gaussian mixture models. In: Field and Service Robotics, pp. 173–187 (2021). Springer
Gálvez-López D, Tardos JD (2012) Bags of binary words for fast place recognition in image sequences. IEEE Transactions on Robotics 28(5):1188–1197
Mur-Artal R, Montiel JMM, Tardos JD (2015) ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE transactions on robotics 31(5):1147–1163
Qin T, Li P, Shen S (2018) VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics 34(4):1004–1020
Zhu, Z., Yang, S., Dai, H., Li, F.: Loop detection and correction of 3D laser-based SLAM with visual information. In: Proceedings of the 31st International Conference on Computer Animation and Social Agents, pp. 53–58 (2018)
Shao, W., Vijayarangan, S., Li, C., Kantor, G.: Stereo visual inertial lidar simultaneous localization and mapping. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 370–377 (2019). IEEE
Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., Rus, D.: Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5135–5142 (2020). IEEE
He, L., Wang, X., Zhang, H.: M2DP: A novel 3D point cloud descriptor and its application in loop closure detection. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 231–237 (2016). IEEE
Kim, G., Kim, A.: Scan context: egocentric spatial descriptor for place recognition within 3D point cloud map. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4802–4809 (2018). IEEE
Rozenberszki, D., Majdik, A.L.: LOL: lidar-only odometry and localization in 3D point cloud maps. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4379–4385 (2020). IEEE
Dubé, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., Cadena, C.: SegMatch: segment based place recognition in 3D point clouds. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5266–5272 (2017). IEEE
Zhou L, Koppel D, Kaess M (2021) LiDAR SLAM with plane adjustment for indoor environment. IEEE Robotics and Automation Letters 6(4):7073–7080
Zhang Y (2021) LILO: a novel lidar-IMU SLAM system with loop optimization. IEEE Transactions on Aerospace and Electronic Systems 58(4):2649–2659
Morris, A., Silver, D., Ferguson, D., Thayer, S.: Towards topological exploration of abandoned mines. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 2117–2123 (2005). IEEE
De Berg, M., Cheong, O., Van Kreveld, M., Overmars, M.: Computational geometry: introduction. Computational geometry: algorithms and applications, 1–17 (2008)
Dellaert F (2012) Factor graphs and GTSAM: a hands-on introduction. Technical report, Georgia Institute of Technology
Dang, T., Khattak, S., Mascarich, F., Alexis, K.: Explore locally, plan globally: a path planning framework for autonomous robotic exploration in subterranean environments. In: 2019 19th International Conference on Advanced Robotics (ICAR), pp. 9–16 (2019). IEEE
Rogers, J.G., Gregory, J.M., Fink, J., Stump, E.: Test your SLAM! the SubT-Tunnel dataset and metric for mapping. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 955–961 (2020). IEEE
David K. Mosch, D.V.P.: Underground opening and support facilities of the Edgar experimental mine Idaho Springs, Colorado. https://www.mines.edu/mining/wp-content/uploads/sites/28/2017/08/edgar-mine-information-2013.pdf (2013)
VersuchsStollen Hagerbach AG: Hagerbach Test Gallery. https://hagerbach.ch/fileadmin/user_upload/hagerbach/downloads/BMT/PDF/preisliste-labor.pdf (2013)
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This work was supported by the Research on the Key Technology of Unmanned Aerial Vehicle in Coal Mine (grant number 2019-TD-2-CXY007).
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Chen, M., Yan, W., Feng, Y. et al. Large-Scale Underground Mine Positioning and Mapping with LiDAR-Based Semantic Intersection Detection. Mining, Metallurgy & Exploration 40, 2007–2021 (2023). https://doi.org/10.1007/s42461-023-00791-5
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DOI: https://doi.org/10.1007/s42461-023-00791-5