Abstract
In this paper, we propose a real-time and low-drift localization method for lidar-equipped robot in indoor environments. State-of-the-art lidar localization research mostly uses a scan-to-scan method, which produces high drifts during the localization of the robot. It is not suitable for robots to operate indoors (such as factory environment) for a long term. Besides, the mapping and localization of this method are susceptible to the dynamic objects (such as pedestrians). To solve above problems, we propose the scan-to-submap matching method for real-time localization. Currently, this method has been used for building maps, and there are few studies to use it for localization, especially for real-time localization. In our research, we build the hardware and software platform for the scan-to-submap matching method. We extensively evaluate our approach with simulations and real-world tests. Compared with the scan-to-scan method, the results demonstrate that our approach can cope with the mapping and localization problem with high localization accuracy and low drift.
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References
Kohlbrecher, S., Von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable SLAM system with full 3d motion estimation. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. pp. 155–160. IEEE (2011)
Grisetti, G., Stachniss, C., Burgard, W., et al.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Rob. 23(1), 34 (2007)
Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2d lidar SLAM. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)
Sun, Z., Wu, B., Xu, C.Z., et al.: Frontier detection and reachability analysis for efficient 2D graph-slam based active exploration. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2017)
Zhang, J., Singh, S.: LOAM: LiDAR odometry and mapping in real-time. In: Proceedings of the Robotics: Science and Systems (RSS), vol. 2, p. 9 (2014)
Filipenko, M., Afanasyev, I.: Comparison of various SLAM systems for mobile robot in an indoor environment. In: 2018 International Conference on Intelligent Systems (IS), pp. 400–407. IEEE (2018)
Yagfarov, R., Ivanou, M., Afanasyev, I.: Map comparison of lidarbased 2d SLAM algorithms using precise ground truth. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1979–1983. IEEE (2018)
Levinson, J., Montemerlo, M., Thrun, S.: Map-based precision vehicle localization in urban environments. In: Proceedings of the Robotics: Science and Systems (RSS), vol. 4, p. 1 (2007)
Wolcott, R.W., Eustice, R.M.: Fast LiDAR localization using multiresolution gaussian mixture maps. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2814–2821 (2015)
Wolcott, R.W., Eustice, R.M.: Robust LiDAR localization using multiresolution gaussian mixture maps for autonomous driving. Int. J. Robot. Res. (IJRR) 36(3), 292–319 (2017)
Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H., Song, S.: Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4670– 4677 (2018)
Park, C., Kim, S., Moghadam, P., Fookes, C., Sridharan, S.: Probabilistic surfel fusion for dense LiDAR mapping. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2418–2426 (2017)
Droeschel, D., Behnke, S.: Efficient continuous-time SLAM for 3D LiDAR-based online mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1–9 (2018)
Liu, H., Ye, Q., Wang, H., Chen, L., Yang, J.: A precise and robust segmentation-based LiDAR localization system for automated urban driving. Remote Sens. 11(11), 1348 (2019)
Li, Q., Chen, S., Wang, C., Li, X., Wen, C., Cheng, M., Li, J.: LONet: deep real-time LiDAR odometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8473–8482 (2019)
Hao, J.: SLAM and navigation robot design based on cartographer algorithm. Shandong University (2019)
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Li, Q., Huai, J., Chen, D., Zhuang, Y. (2021). Real-Time Robot Localization Based on 2D Lidar Scan-to-Submap Matching. In: Yang, C., Xie, J. (eds) China Satellite Navigation Conference (CSNC 2021) Proceedings. Lecture Notes in Electrical Engineering, vol 773. Springer, Singapore. https://doi.org/10.1007/978-981-16-3142-9_39
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DOI: https://doi.org/10.1007/978-981-16-3142-9_39
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