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...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2020
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| Summary: | 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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