MSC-LIO：一种基于 MSCKF 的具有同平面簇跟踪的激光雷达-惯性里程计
MSC-LIO: An MSCKF-Based LiDAR-Inertial Odometry with Same-Plane Cluster Tracking
AI总结：
本文提出一种基于 MSCKF 框架的紧耦合激光雷达-惯性里程计 MSC-LIO，通过无需特征提取的同平面簇跟踪方法构建多状态约束并估计时间延迟，在精度、效率和实时性上优于现有方法。
摘要：
多状态约束卡尔曼滤波器（MSCKF）已被证明在视觉里程计中比图优化更高效，且具有相似的精度。然而，对于激光雷达里程计，它尚未得到充分的考虑和研究。在本文中，我们提出了一种基于 MSCKF 框架的新型紧耦合激光雷达-惯性里程计，名为 MSC-LIO。我们提出了一种高效的激光雷达同平面簇（LSPC）跟踪方法，无需显式特征提取，用于帧间数据关联。被跟踪的 LSPC 用于构建 LSPC 测量模型，该模型构建多状态约束。此外，我们提出了一种有效的基于点速度的激光雷达-IMU 时间延迟（LITD）估计方法，该方法由所提出的 LSPC 跟踪方法推导而来。为了验证所提方法的有效性和鲁棒性，我们在公共数据集和真实世界环境中进行了大量实验。结果表明，与最先进的方法相比，所提出的 MSC-LIO 具有更高的精度和效率。消融实验表明，使用 LSPC 跟踪使数据关联效率提高了近 3 倍，并且所提出的 LITD 估计方法能够有效且准确地估计 LITD。此外，MSC-LIO 被部署在边缘设备上，并展示了出色的实时性能。
英文摘要：
The multi-state constraint Kalman filter (MSCKF) has been proven to be more efficient than graph optimization for visual-based odometry while with similar accuracy. However, it has not been adequately considered and studied for LiDAR-based odometry. In this paper, we propose a novel tightly-coupled LiDAR-inertial odometry based on the MSCKF framework, named MSC-LIO. An efficient LiDAR same-plane cluster (LSPC) tracking method, without explicit feature extraction, is present for frame-to-frame data associations. The tracked LSPC is used to build an LSPC measurement model that constructs multi-state constraints. Besides, we propose an effective point-velocity-based LiDAR-IMU time-delay (LITD) estimation method, which is derived from the proposed LSPC tracking method. To validate the effectiveness and robustness of the proposed method, we conducted extensive experiments on both public datasets and real-world environments. The results demonstrate that the proposed MSC-LIO yields higher accuracy and efficiency compared to the state-of-the-art methods. Ablation experiments indicate that the data-association efficiency is improved by nearly 3 times with the LSPC tracking, and the proposed LITD estimation method can effectively and accurately estimate the LITD. Besides, MSC-LIO was implemented on an edge device and demonstrated excellent real-time performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
