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arXiv · 2206.10517

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

Roberto Aldera·Matthew Gadd·Daniele De Martini·Paul Newman
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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.

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Roberto Aldera, Matthew Gadd, Daniele De Martini, Paul Newman. 2022-06-21. What Goes Around: Leveraging a Constant-curvature Motion Constraint in Radar Odometry. https://arxiv.org/abs/2206.10517

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