LiDAR Odometry is an essential component in many robotic applications. Unlike the mainstreamed approaches that focus on improving the accuracy by the additional inertial sensors, this letter explores the capability of LiDAR-only odometry through a continuous-time perspective. Firstly, the measurements of LiDAR are regarded as streaming points continuously captured at high frequency. Secondly, the LiDAR movement is parameterized by a simple yet effective continuous-time trajectory. Therefore, our proposed Traj-LO approach tries to recover the spatial-temporal consistent movement of LiDAR by tightly coupling the geometric information from LiDAR points and kinematic constraints from trajectory smoothness. This framework is generalized for different kinds of LiDAR as well as multi-LiDAR systems. Extensive experiments on the public datasets demonstrate the robustness and effectiveness of our proposed LiDAR-only approach, even in scenarios where the kinematic state exceeds the IMU's measuring range. Our implementation is open-sourced on GitHub.

翻译：激光雷达里程计是众多机器人应用中的关键组成部分。与主流方法依赖额外惯性传感器提升精度不同，本文从连续时间视角探索纯激光雷达里程计的潜力。首先，将激光雷达的测量视为高频连续采集的流式点云数据；其次，利用一种简单而有效的连续时间轨迹对激光雷达运动进行参数化建模。由此，我们提出的Traj-LO方法通过紧密耦合激光雷达点的几何信息与轨迹平滑性带来的运动学约束，恢复激光雷达时空一致的运动。该框架可推广至不同类型激光雷达及多激光雷达系统。在公开数据集上的大量实验表明，即使在运动学状态超出IMU测量范围的场景中，该纯激光雷达方法仍展现出鲁棒性与有效性。本实现已在GitHub上开源。