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

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 nonholonomic 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 pointbased odometry implementations that rely on a range sensor and provides a lightweight and interpretable means of incorporating vehicle dynamics for ego-motion estimation.

R3O: Robust Radon Radar Odometry

“…Another approach using neural networks and supervised learning was proposed by Barnes et al [7]. Furthermore, Aldera et al proposed in [8] a mechanism to detect failures patterns in the feature matching step and in [9] a smooth-curvature constraint based on Ackermann drive model was exploited. Recently, Hyungtae et al presented the ORORA algorithm which uses anisotropic component-wise translation estimation and graduated non-convexity based rotation estimation to estimate the rotation and translation separately.…”