Advancements in Radar Odometry

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 and simultaneously present an understanding about the current advancements in radar odometry estimation. First, we propose novel improvements to an existing state-of-the-art method, which are 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. In particular, we enforce local understanding of a scene by including additional information through smoothing (Gaussian kernels) and alignment (ICP), introduced by us in the existing pipeline. Then, we present an in-depth investigation of the contribution of each improvement to the localization accuracy. Lastly, we benchmark our system and state-of-the-art methods on all sequences of well-known datasets for radar understanding, i.e., the Oxford Radar RobotCar, MulRan, and Boreas datasets. In particular, Boreas includes scenarios with challenging weather conditions, such as snow or overcast, and, to our knowledge, it has never been used for evaluation or benchmarking in the literature. The effectiveness of the proposed improvements is proven by an increased translation and rotation accuracy on the majority of scenarios considered.