Compact wearable mapping system (WMS) has gained significant attention due to their convenience in various applications. Specifically, it provides an efficient way to collect prior maps for 3D structure inspection and robot-based "last-mile delivery" in complex environments. However, vibrations in human motion and the uneven distribution of point cloud features in complex environments often lead to rapid drift, which is a prevalent issue when applying existing LiDAR Inertial Odometry (LIO) methods on low-cost WMS. To address these limitations, we propose a novel LIO for WMSs based on Hybrid Continuous Time Optimization (HCTO) considering the optimality of Lidar correspondences. First, HCTO recognizes patterns in human motion (high-frequency part, low-frequency part, and constant velocity part) by analyzing raw IMU measurements. Second, HCTO constructs hybrid IMU factors according to different motion states, which enables robust and accurate estimation against vibration-induced noise in the IMU measurements. Third, the best point correspondences are selected using optimal design to achieve real-time performance and better odometry accuracy. We conduct experiments on head-mounted WMS datasets to evaluate the performance of our system, demonstrating significant advantages over state-of-the-art methods. Video recordings of experiments can be found on the project page of HCTO: \href{https://github.com/kafeiyin00/HCTO}{https://github.com/kafeiyin00/HCTO}.

翻译：紧凑可穿戴测绘系统（WMS）因其在多种应用场景中的便捷性而受到广泛关注。具体而言，它为在复杂环境下进行三维结构检测和基于机器人的“最后一公里配送”提供了一种高效采集先验地图的方式。然而，人体运动中的振动以及复杂环境中点云特征分布不均匀常导致快速漂移，这是现有激光雷达惯性里程计（LIO）方法应用于低成本WMS时的普遍问题。为解决这些局限，我们提出了一种基于混合连续时间优化（HCTO）的新型LIO方法，该方法考虑了激光雷达对应关系的最优性。首先，HCTO通过分析原始IMU测量值识别人体运动模式（高频部分、低频部分和匀速部分）。其次，HCTO根据不同的运动状态构建混合IMU因子，从而能够针对IMU测量中振动引起的噪声实现鲁棒且准确的估计。第三，利用最优设计选择最佳点对应关系，以实现实时性能和更优的里程计精度。我们在头戴式WMS数据集上开展实验评估系统性能，结果表明该方法相较于现有最优方法具有显著优势。实验视频记录可在HCTO项目页面获取：\href{https://github.com/kafeiyin00/HCTO}{https://github.com/kafeiyin00/HCTO}。