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 yet been properly 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-point (LSPP) tracking method, without explicit feature extraction, is present for frame-to-frame data associations. The tracked LSPPs are employed to build an LSPP measurement model, which constructs a multi-state constraint. Besides, we propose an effective point-velocity-based LiDAR-IMU time-delay (LITD) estimation method, which is derived from the proposed LSPP tracking method. Extensive experiments were conducted on both public and private datasets. The results demonstrate that the proposed MSC-LIO yields higher accuracy and efficiency than the state-of-the-art methods. The ablation experiment results indicate that the data-association efficiency is improved by nearly 3 times using the LSPP tracking method. Besides, the proposed LITD estimation method can effectively and accurately estimate the LITD.

翻译：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方法更高效，同时保持相似的精度。然而，该方法尚未在基于激光雷达的里程计中得到充分考虑和研究。本文提出了一种基于MSCKF框架的新型紧耦合激光雷达-惯性里程计，命名为MSC-LIO。我们提出了一种高效的激光雷达同平面点（LSPP）跟踪方法，无需显式特征提取，用于实现帧间数据关联。所跟踪的LSPP被用于构建LSPP测量模型，该模型形成了多状态约束。此外，我们提出了一种基于点速度的有效激光雷达-IMU时间延迟（LITD）估计方法，该方法源自所提出的LSPP跟踪方法。我们在公开和私有数据集上进行了大量实验。结果表明，所提出的MSC-LIO比现有最先进方法具有更高的精度和效率。消融实验结果表明，使用LSPP跟踪方法可将数据关联效率提高近3倍。此外，所提出的LITD估计方法能够有效且准确地估计LITD。