Abstract
At the present stage, LiDAR-based SLAM solutions are dominated by ICP and its variants, while the BA optimization method that can improve the pose consistency has received little attention. Therefore, we propose MULO, a low-drift and robust LiDAR odometry using BA optimization with plane and cylinder landmarks. In the front-end, a coarse-to-fine direct pose estimation method provides the prior pose to the back-end. And in the back-end, we propose a novel three-stage landmark extraction and data association strategy for plane and cylinder, which is robust and efficient. Meanwhile, a stable minimum parameterization method for cylinder landmarks is proposed for optimization. In order to fully utilize the LiDAR information at long distances, we propose a new sliding window structure consisting of a TinyWindow and a SuperWindow. Finally, we jointly optimize the two kinds of landmarks and scan poses in this sliding window. The proposed system is evaluated on public dataset and our dataset, and experimental results show that our system is competitive compared with the state-of-the-art LiDAR odometrys.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Code Availibility
Not applicable
Availability of Data and Materials
Not applicable
References
Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–606 (1992). Spie
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
Wang, H., Wang, C., Chen, C.-L., Xie, L.: F-loam: fast lidar odometry and mapping. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4390–4396 (2021). IEEE
Pan, Y., Xiao, P., He, Y., Shao, Z., Li, Z.: Mulls: versatile lidar slam via multi-metric linear least square. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 11633–11640 (2021). IEEE
Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M.: Learning informative point classes for the acquisition of object model maps. In: 2008 10th International Conference on Control, Automation, Robotics and Vision, pp. 643–650 (2008). IEEE
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 (2009). IEEE
Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3d recognition and pose using the viewpoint feature histogram. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155–2162 (2010). IEEE
Liu, Z., Zhang, F.: Balm: bundle adjustment for lidar mapping. IEEE Robotics Automation Lett. 6(2), 3184–3191 (2021)
Geneva, P., Eckenhoff, K., Yang, Y., Huang, G.: Lips: lidar-inertial 3d plane slam. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 123–130 (2018). IEEE
Ferrer, G.: Eigen-factors: plane estimation for multi-frame and time-continuous point cloud alignment. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1278–1284 (2019). IEEE
Zhou, L., Koppel, D., Ju, H., Steinbruecker, F., Kaess, M.: An efficient planar bundle adjustment algorithm. In: 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 136–145 (2020). IEEE
Zhou, L., Koppel, D., Kaess, M.: Lidar slam with plane adjustment for indoor environment. IEEE Robotics Automation Lett. 6(4), 7073–7080 (2021)
Huang, H., Sun, Y., Wu, J., Jiao, J., Hu, X., Zheng, L., Wang, L., Liu, M.: On bundle adjustment for multiview point cloud registration. IEEE Robotics Automation Lett. 6(4), 8269–8276 (2021)
Zhou, L., Wang, S., Kaess, M.: \(\pi \)-lsam: lidar smoothing and mapping with planes. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 5751–5757 (2021). IEEE
Wen, W., Zhou, Y., Zhang, G., Fahandezh-Saadi, S., Bai, X., Zhan, W., Tomizuka, M., Hsu, L.-T.: Urbanloco: a full sensor suite dataset for mapping and localization in urban scenes. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 2310–2316 (2020). IEEE
Zuo, X., Yang, Y., Geneva, P., Lv, J., Liu, Y., Huang, G., Pollefeys, M.: Lic-fusion 2.0: Lidar-inertial-camera odometry with sliding-window plane-feature tracking. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5112–5119 (2020). IEEE
Lee, W., Yang, Y., Huang, G.: Efficient multi-sensor aided inertial navigation with online calibration. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 5706–5712 (2021). IEEE
Xu, W., Zhang, F.: Fast-lio: a fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter. IEEE Robotics Automation Lett. 6(2), 3317–3324 (2021)
Funding
Not applicable
Author information
Authors and Affiliations
Contributions
In this work, Wei Liu (corresponding author) coordinated the overall project and group activities. Jun Liu was responsible for the theoretical derivation and code writing, and Zhengnan He was responsible for the code writing and experimental design, and both of them jointly proposed the innovative points and wrote the theoretical derivation part of the manuscript. Zhao Xiaoyu was responsible for the experimental platform construction and experiments, and wrote the experimental part of the manuscript. Jun Hu and Shuai Cheng supplemented the experiments and wrote the other parts of the manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
The authors have no relevant financial or non-financial interests to disclose
Ethics Approval
No ethical approval is required by this research.
Consent to Participate
Not applicable
Consent for Publication
This paper does not require any consent for publication.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Liu, J., He, Z., Zhao, X. et al. MULO: LiDAR Odometry via MUlti-Landmarks Joint Optimization. J Intell Robot Syst 110, 146 (2024). https://doi.org/10.1007/s10846-024-02172-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10846-024-02172-6