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This paper presents a self-supervised framework for learning to detect robustkeypoints for odometry estimation and metric localisation in radar. Byembedding a differentiable point-based motion estimator inside ourarchitecture, we learn keypoint locations, scores and descriptors fromlocalisation error alone. This approach avoids imposing any assumption on whatmakes a robust keypoint and crucially allows them to be optimised for ourapplication. Furthermore the architecture is sensor agnostic and can be appliedto most modalities. We run experiments on 280km of real world driving from theOxford Radar RobotCar Dataset and improve on the state-of-the-art inpoint-based radar odometry, reducing errors by up to 45% whilst running anorder of magnitude faster, simultaneously solving metric loop closures.Combining these outputs, we provide a framework capable of full mapping andlocalisation with radar in urban environments.

Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar