D-LIO：基于同时截断距离场映射的6自由度直接激光雷达-惯性里程计
D-LIO: 6DoF Direct LiDAR-Inertial Odometry based on Simultaneous Truncated Distance Field Mapping
AI总结：
D-LIO通过同时截断距离场映射实现6自由度直接激光雷达-惯性里程计，提升环境感知精度与实时性。
摘要：
本文提出了一种基于CPU上同时映射截断距离场的新的6自由度直接激光雷达-惯性里程计（D-LIO）方法。这种连续表示（在点周围）使能够在线处理原始3D激光雷达数据，避免了激光雷达特征选择和跟踪的需要，简化了里程计流程，并且可以轻松推广到许多场景。该方法基于所提出的快速截断距离场（Fast-TDF）方法作为方便的工具来表示环境。这种表示方法使i）将激光雷达点云配准作为非线性优化过程，而无需在输入数据中选择/跟踪激光雷达特征；ii）同时生成准确的截断距离场地图；iii）以恒定时间更新此类地图，无论其大小如何。该方法使用公开数据集进行测试，包括航空和地面数据。它还与最先进的里程计方法进行了基准测试，证明了与之相同或更好的精度水平，同时具有额外的价值，即能够为其他机器人任务如规划或避障提供在线生成的环境TDF表示。源代码在https://anonymous.4open.science/r/D-LIO公开可用。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
