What Goes Around: Leveraging a Constant-curvature Motion Constraint in Radar Odometry

TL;DR

This paper introduces a method that uses vehicle motion constraints to improve radar odometry accuracy by focusing on single landmark associations, reducing outliers and halving odometry error.

Contribution

It develops a framework leveraging constant-curvature motion constraints to refine data associations in radar odometry, enhancing accuracy and outlier detection.

Findings

Translational error decreased by 2.15%

Odometry error was approximately halved

Method provides a lightweight, interpretable vehicle dynamics incorporation

Abstract

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.