雷达里程计的进展
Advancements in Radar Odometry
AI总结：
本文改进现有雷达里程计方法，构建含滤波、运动补偿、平滑、一对多配准与位姿细化的流程，并在 Oxford Radar RobotCar、MulRan 和 Boreas 上验证其精度与鲁棒性。
摘要：
雷达里程计估计已成为自主导航领域的一项关键技术，可在各种环境条件下提供鲁棒且可靠的运动估计。尽管具有潜力，但雷达信号的复杂性质以及处理这些信号所固有的挑战限制了该技术的广泛采用。本文旨在通过对现有雷达里程计估计方法提出新颖改进来应对这些挑战，这些改进旨在提高不同场景下的准确性和可靠性。我们的流程包括滤波、运动补偿、定向表面点计算、平滑、一对多雷达扫描配准以及位姿细化。所开发方法通过平滑技术添加额外信息，并将连续扫描对齐作为一对多配准之后的细化步骤，从而增强对场景的局部理解。我们深入研究了每项改进对定位精度的贡献，并在用于雷达理解的主要数据集（即 Oxford Radar RobotCar、MulRan 和 Boreas 数据集）的序列上对我们的系统进行了基准测试。所提出的流程能够在所考虑的所有场景以及恶劣环境约束下取得优异结果。
英文摘要：
Radar odometry estimation has emerged as a critical technique in the field of autonomous navigation, providing robust and reliable motion estimation under various environmental conditions. Despite its potential, the complex nature of radar signals and the inherent challenges associated with processing these signals have limited the widespread adoption of this technology. This paper aims to address these challenges by proposing novel improvements to an existing method for radar odometry estimation, designed to enhance accuracy and reliability in diverse scenarios. Our pipeline consists of filtering, motion compensation, oriented surface points computation, smoothing, one-to-many radar scan registration, and pose refinement. The developed method enforces local understanding of the scene, by adding additional information through smoothing techniques, and alignment of consecutive scans, as a refinement posterior to the one-to-many registration. We present an in-depth investigation of the contribution of each improvement to the localization accuracy, and we benchmark our system on the sequences of the main datasets for radar understanding, i.e., the Oxford Radar RobotCar, MulRan, and Boreas datasets. The proposed pipeline is able to achieve superior results, on all scenarios considered and under harsh environmental constraints.
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。
