Abstract
Accurate and robust map-based localization is crucial for autonomous mobile robots. In this paper, we build a set of lidar localization framework, from initialization to pose tracking, to achieve real-time localization of mobile robots on priori map. To tackle the problem that large localization errors happen when mobile robot is turning fastly, we proposes a priori pose compensation method to improve it. By extracting the global features of the point cloud and performing pre-alignment, we provide an accurate a prior pose for further point cloud registration to improve the accuracy of robot localization in turns. We tested the proposed localization approach on a mobile robot platform equipped with a lidar sensor in campus scenario. The experimental results show that our method can reliably and accurately localize the mobile robot in the campus scenario and operate online at the lidar sensor frame rate to track the robot pose.
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References
Gao, Y., Liu, S., Atia, M.M., Noureldin, A.: INS/GPS/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm. Sensors 15, 23286–23302 (2015)
Nakhaeinia, D., Tang, S.H., Noor, S.M., Motlagh, O.: A review of control architectures for autonomous navigation of mobile robots. Int. J. Phys. Sci. 6, 169–174 (2011)
Yin, H., Wang, Y., Ding, X., Tang, L., Huang, S., Xiong, R.: 3d lidar-based global localization using siamese neural network. IEEE Trans. Intell. Transp. Syst. 21, 1380–1392 (2019)
Weiss, U., Biber, P.: Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Robot. Auton. Syst. 59, 265–273 (2011)
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. IEEE (2017)
Meierhold, N., Spehr, M., Schilling, A., Gumhold, S., Maas, H.: Automatic feature matching between digital images and 2D representations of a 3D laser scanner point cloud. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38, 446–451 (2010)
Bosse, M., Zlot, R.: Map matching and data association for large-scale two-dimensional laser scan-based slam. Int. J. Robot. Res. 27, 667–691 (2008)
Li, B., Zhang, T., Xia, T.: Vehicle detection from 3d lidar using fully convolutional network (2016). arXiv preprint arXiv:1608.07916
Choi, S., Choi, S., Kim, C.: MobileHumanPose: toward real-time 3D human pose estimation in mobile devices. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2328–2338 (2021)
Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 466–481 (2018)
Liu, G.-X., Shi, L.-F., Chen, S., Wu, Z.-G.: Focusing matching localization method based on indoor magnetic map. IEEE Sens. J. 20, 10012–10020 (2020)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE (2009)
Akai, N., Morales, L.Y., Takeuchi, E., Yoshihara, Y., Ninomiya, Y.: Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching. In: 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1356–1363. IEEE (2017)
Fu, H., Ye, L., Yu, R., Wu, T.: An efficient scan-to-map matching approach for autonomous driving. In: 2016 IEEE International Conference on Mechatronics and Automation, pp. 1649–1654. IEEE (2016)
Huang, G.P., Mourikis, A.I., Roumeliotis, S.I.: A quadratic-complexity observability-constrained unscented Kalman filter for SLAM. IEEE Trans. Rob. 29, 1226–1243 (2013)
Choi, S., Hong, D.: Position estimation in urban U-turn section for autonomous vehicles using multiple vehicle model and interacting multiple model filter. Int. J. Automot. Technol. 22, 1599–1607 (2021)
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. IEEE (2018)
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This work was supported by the National Natural Science Found of china (Grant No. 62103393).
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Zhao, G., Wang, J., Chen, L., Chen, Z. (2022). Lidar Localization Method for Mobile Robots Based on Priori Pose Compensation. In: Fan, W., Zhang, L., Li, N., Song, X. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2022. Communications in Computer and Information Science, vol 1713. Springer, Singapore. https://doi.org/10.1007/978-981-19-9195-0_26
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DOI: https://doi.org/10.1007/978-981-19-9195-0_26
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