arXiv 2307.077922024-07-04cs.RO

半弹性激光雷达-惯性里程计

Semi-Elastic LiDAR-Inertial Odometry

Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Hangzhou Guochen Robot Technology Company Limited（杭州国辰机器人科技有限公司）

Zikang Yuan, Fengtian Lang, Tianle Xu, Ruiye Ming, Chengwei Zhao, Xin Yang

备注：: 8 pages, planning to submit to RA-L. arXiv admin note: substantial text overlap with arXiv:2210.10424. text overlap with arXiv:2302.14298

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AI总结：

针对现有激光雷达-惯性状态估计因扫描间状态假设导致的局部不一致问题，本文提出半弹性优化方法，赋予状态足够弹性以优化到正确值，并在四个公开数据集上验证了其在精度、一致性和鲁棒性上优于现有方法。

摘要：

现有的激光雷达-惯性状态估计假设当前扫描开始时的状态与上一次扫描结束时的状态相同。然而，如果上一次扫描结束时的状态不准确，当前状态就无法一致地满足来自激光雷达和IMU的约束，最终导致求解状态的局部不一致（例如锯齿形轨迹或高频振荡速度）。本文提出了一种基于半弹性优化的激光雷达-惯性状态估计方法，该方法赋予状态足够的弹性，使其能够被优化到正确的值。这种方法能够更好地保证状态估计的准确性、一致性和鲁棒性。我们将所提出的激光雷达-惯性状态估计方法集成到一个基于优化的激光雷达-惯性里程计（LIO）框架中。在四个公开数据集上的实验结果表明：1）我们的方法在精度上优于现有的最先进的激光雷达-惯性里程计系统；2）与传统的和基于弹性优化的激光雷达-惯性状态估计相比，基于半弹性优化的激光雷达-惯性状态估计能够更好地保证一致性和鲁棒性。我们已发布本工作的源代码，以促进社区的发展。

英文摘要：

Existing LiDAR-inertial state estimation assumes that the state at the beginning of current sweep is identical to the state at the end of last sweep. However, if the state at the end of last sweep is not accurate, the current state cannot satisfy the constraints from LiDAR and IMU consistently, ultimately resulting in local inconsistency of solved state (e.g., zigzag trajectory or high-frequency oscillating velocity). This paper proposes a semi-elastic optimization-based LiDAR-inertial state estimation method, which imparts sufficient elasticity to the state to allow it be optimized to the correct value. This approach can preferably ensure the accuracy, consistency, and robustness of state estimation. We incorporate the proposed LiDAR-inertial state estimation method into an optimization-based LiDAR-inertial odometry (LIO) framework. Experimental results on four public datasets demonstrate that: 1) our method outperforms existing state-of-the-art LiDAR-inertial odometry systems in terms of accuracy; 2) semi-elastic optimization-based LiDAR-inertial state estimation can better ensure consistency and robustness than traditional and elastic optimization-based LiDAR-inertial state estimation. We have released the source code of this work for the development of the community.

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