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.

翻译：雷达里程计估计已成为自主导航领域的关键技术，能在多种环境条件下提供稳健可靠的运动估计。尽管具有潜力，但雷达信号的复杂特性以及处理这些信号固有的挑战限制了该技术的广泛应用。本文旨在通过提出对现有雷达里程计估计方法的创新性改进来应对这些挑战，旨在提升不同场景下的精度与可靠性。我们的管道包含滤波、运动补偿、定向表面点计算、平滑处理、一对多雷达扫描配准以及位姿精细化。所开发的方法通过平滑技术引入额外信息以增强对场景的局部理解，并通过对连续扫描的对齐（作为一对多配准后的精细化后处理步骤）来提升性能。我们深入研究了每项改进对定位精度的贡献，并在雷达理解的主要数据集序列（即Oxford Radar RobotCar、MulRan和Boreas数据集）上对系统进行了基准测试。所提出的管道在考虑的所有场景及严苛环境约束下均能实现优越结果。