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: .

中文翻译：

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HCTO：具有混合连续时间优化的优化感知 LiDAR 惯性里程计，适用于紧凑型可穿戴测绘系统

紧凑型可穿戴地图系统（WMS）因其在各种应用中的便利性而受到广泛关注。具体来说，它提供了一种有效的方法来收集先前的地图，用于复杂环境中的 3D 结构检查和基于机器人的“最后一英里交付”。然而，复杂环境中人体运动的振动和点云特征的不均匀分布往往会导致快速漂移，这是在低成本WMS上应用现有激光雷达惯性里程计（LIO）方法时普遍存在的问题。为了解决这些限制，考虑到激光雷达对应的最优性，我们提出了一种基于混合连续时间优化（HCTO）的新型 WMS LIO。首先，HCTO 通过分析原始 IMU 测量结果来识别人体运动模式（高频部分、低频部分和等速部分）。其次，HCTO 根据不同的运动状态构建混合 IMU 因子，从而能够对 IMU 测量中的振动引起的噪声进行稳健且准确的估计。第三，使用优化设计选择最佳点对应关系，以实现实时性能和更好的里程计精度。我们在头戴式 WMS 数据集上进行了实验，以评估我们系统的性能，展示了相对于最先进方法的显着优势。实验视频记录可以在HCTO的项目页面上找到： 。