Fast radar motion estimation with a learnt focus of attention using weak supervision

This paper is about fast motion estimation with scanning radar. We use weak supervision to train a focus of attention policy which actively down-samples the measurement stream before data association steps are undertaken. At training, we avoid laborious manual labelling by exploiting short-term sens...

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Main Authors: Aldera, R, De Martini, D, Gadd, M, Newman, p
格式: Conference item
出版: IEEE 2019
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author Aldera, R
De Martini, D
Gadd, M
Newman, p
author_facet Aldera, R
De Martini, D
Gadd, M
Newman, p
author_sort Aldera, R
collection OXFORD
description This paper is about fast motion estimation with scanning radar. We use weak supervision to train a focus of attention policy which actively down-samples the measurement stream before data association steps are undertaken. At training, we avoid laborious manual labelling by exploiting short-term sensor coherence from multiple poses in the presence of an external ego-motion estimator (for example, wheel odometry). In this way, we generate copious annotated measurements which can be used for training a learning algorithm in a weakly-supervised fashion. We demonstrate the validity of the approach in the context of a Radar Odometry (RO) task, pre-filtering raw data with a popular image segmentation network trained as presented. We evaluate our system against 26 km of data collected in Central Oxford and show consistent motion estimation with greatly reduced radar processing times (by a factor of 2.36).
first_indexed 2024-03-07T06:27:22Z
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institution University of Oxford
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publishDate 2019
publisher IEEE
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spelling oxford-uuid:f4c7ab3c-6b49-4b8f-b4dc-a830085529862022-03-27T12:22:16ZFast radar motion estimation with a learnt focus of attention using weak supervisionConference itemhttp://purl.org/coar/resource_type/c_5794uuid:f4c7ab3c-6b49-4b8f-b4dc-a83008552986Symplectic Elements at OxfordIEEE2019Aldera, RDe Martini, DGadd, MNewman, pThis paper is about fast motion estimation with scanning radar. We use weak supervision to train a focus of attention policy which actively down-samples the measurement stream before data association steps are undertaken. At training, we avoid laborious manual labelling by exploiting short-term sensor coherence from multiple poses in the presence of an external ego-motion estimator (for example, wheel odometry). In this way, we generate copious annotated measurements which can be used for training a learning algorithm in a weakly-supervised fashion. We demonstrate the validity of the approach in the context of a Radar Odometry (RO) task, pre-filtering raw data with a popular image segmentation network trained as presented. We evaluate our system against 26 km of data collected in Central Oxford and show consistent motion estimation with greatly reduced radar processing times (by a factor of 2.36).
spellingShingle Aldera, R
De Martini, D
Gadd, M
Newman, p
Fast radar motion estimation with a learnt focus of attention using weak supervision
title Fast radar motion estimation with a learnt focus of attention using weak supervision
title_full Fast radar motion estimation with a learnt focus of attention using weak supervision
title_fullStr Fast radar motion estimation with a learnt focus of attention using weak supervision
title_full_unstemmed Fast radar motion estimation with a learnt focus of attention using weak supervision
title_short Fast radar motion estimation with a learnt focus of attention using weak supervision
title_sort fast radar motion estimation with a learnt focus of attention using weak supervision
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AT demartinid fastradarmotionestimationwithalearntfocusofattentionusingweaksupervision
AT gaddm fastradarmotionestimationwithalearntfocusofattentionusingweaksupervision
AT newmanp fastradarmotionestimationwithalearntfocusofattentionusingweaksupervision