  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.
