用截断距离场实现无需特征提取的实时激光惯性里程计
D-LIO: 6DoF Direct LiDAR-Inertial Odometry based on Simultaneous Truncated Distance Field Mapping
- 基于快速截断距离场构建环境连续表示
- 在公开数据集上达到与顶尖方法相当的精度
- 适合需要在线地图构建的自动驾驶与机器人导航
本文提出一种基于截断距离场（TDF）的6自由度直接激光惯性里程计（D-LIO），可在CPU上实时完成环境的连续表示。该方法利用快速截断距离场（Fast-TDF）对三维激光点云进行建模，避免了传统方法中特征选择与跟踪的复杂流程，将点云配准转化为非线性优化问题，同时生成高精度的环境TDF地图，并以恒定时间更新，不受地图规模影响。在地面和空中开源数据集上的实验表明，该方法在精度上达到或超过现有先进方法，且能在线生成可用于路径规划与避障等任务的环境地图。源代码已公开。
原文摘要 · Abstract (English)
This paper presents a new approach for 6DoF Direct LiDAR-Inertial Odometry (D-LIO) based on the simultaneous mapping of truncated distance fields on CPU. Such continuous representation (in the vicinity of the points) enables working with raw 3D LiDAR data online, avoiding the need of LiDAR feature selection and tracking, simplifying the odometry pipeline and easily generalizing to many scenarios. The method is based on the proposed Fast Truncated Distance Field (Fast-TDF) method as a convenient tool to represent the environment. Such representation enables i) solving the LiDAR point-cloud registration as a nonlinear optimization process without the need of selecting/tracking LiDAR features in the input data, ii) simultaneously producing an accurate truncated distance field map of the environment, and iii) updating such map at constant time independently of its size. The approach is tested using open datasets, aerial and ground. It is also benchmarked against other state-of-the-art odometry approaches, demonstrating the same or better level of accuracy with the added value of an online-generated TDF representation of the environment, that can be used for other robotics tasks as planning or collision avoidance. The source code is publicly available at https://anonymous.4open.science/r/D-LIO
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