  This paper presents a method that leverages vehicle motion constraints to
refine data associations in a point-based radar odometry system. By using the
strong prior on how a non-holonomic robot is constrained to move smoothly
through its environment, we develop the necessary framework to estimate
ego-motion from a single landmark association rather than considering all of
these correspondences at once. This allows for informed outlier detection of
poor matches that are a dominant source of pose estimate error. By refining the
subset of matched landmarks, we see an absolute decrease of 2.15% (from 4.68%
to 2.53%) in translational error, approximately halving the error in odometry
(reducing by 45.94%) than when using the full set of correspondences. This
contribution is relevant to other point-based odometry implementations that
rely on a range sensor and provides a lightweight and interpretable means of
incorporating vehicle dynamics for ego-motion estimation.
