Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar

This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable point-based motion estimator inside our architecture, we learn keypoint locations, scores and descriptors from localisation...

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Main Authors: Barnes, D, Posner, H
Format: Conference item
Language:English
Published: IEEE 2020
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author Barnes, D
Posner, H
author_facet Barnes, D
Posner, H
author_sort Barnes, D
collection OXFORD
description This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable point-based motion estimator inside our architecture, we learn keypoint locations, scores and descriptors from localisation error alone. This approach avoids imposing any assumption on what makes a robust keypoint and crucially allows them to be optimised for our application. Furthermore the architecture is sensor agnostic and can be applied to most modalities. We run experiments on 280km of real world driving from the Oxford Radar RobotCar Dataset and improve on the state-of-the-art in point-based radar odometry, reducing errors by up to 45% whilst running an order of magnitude faster, simultaneously solving metric loop closures. Combining these outputs, we provide a framework capable of full mapping and localisation with radar in urban environments.
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spelling oxford-uuid:c9e60324-1fd1-4e84-b7b0-0d671bd103a62022-03-27T07:03:22ZUnder the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radarConference itemhttp://purl.org/coar/resource_type/c_5794uuid:c9e60324-1fd1-4e84-b7b0-0d671bd103a6EnglishSymplectic ElementsIEEE2020Barnes, DPosner, HThis paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable point-based motion estimator inside our architecture, we learn keypoint locations, scores and descriptors from localisation error alone. This approach avoids imposing any assumption on what makes a robust keypoint and crucially allows them to be optimised for our application. Furthermore the architecture is sensor agnostic and can be applied to most modalities. We run experiments on 280km of real world driving from the Oxford Radar RobotCar Dataset and improve on the state-of-the-art in point-based radar odometry, reducing errors by up to 45% whilst running an order of magnitude faster, simultaneously solving metric loop closures. Combining these outputs, we provide a framework capable of full mapping and localisation with radar in urban environments.
spellingShingle Barnes, D
Posner, H
Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
title Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
title_full Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
title_fullStr Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
title_full_unstemmed Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
title_short Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
title_sort under the radar learning to predict robust keypoints for odometry estimation and metric localisation in radar
work_keys_str_mv AT barnesd undertheradarlearningtopredictrobustkeypointsforodometryestimationandmetriclocalisationinradar
AT posnerh undertheradarlearningtopredictrobustkeypointsforodometryestimationandmetriclocalisationinradar