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

翻译：本文提出了一种基于CPU同步建图的截断距离场6自由度直接激光雷达-惯性里程计新方法。这种连续表示（在点云邻近区域）能够在线处理原始3D激光雷达数据，避免了激光雷达特征选择与跟踪的需求，简化了里程计流程，并易于推广至多种场景。该方法基于所提出的快速截断距离场方法作为环境表示的便捷工具。该表示方式能够：i) 将激光雷达点云配准作为非线性优化过程求解，无需在输入数据中选择/跟踪激光雷达特征；ii) 同步生成精确的环境截断距离场地图；iii) 以恒定时间更新地图，且与地图尺寸无关。该方法通过公开数据集、空中及地面数据进行测试，并与其它先进里程计方法进行基准比较，在保持相同或更高精度的同时，额外提供在线生成的TDF环境表示，该表示可应用于路径规划或碰撞避免等其它机器人任务。源代码公开于：https://anonymous.4open.science/r/D-LIO