End-to-end Neural Estimation of Spacecraft Pose with Intermediate Detection of Keypoints
(2022) European Conference on Computer Vision, Workshop on AI for Space 2022 (23.October.2022)
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- Abstract
- State-of-the-art methods for estimating the pose of spacecrafts in Earth-orbit images rely on a convolutional neural network either to directly regress the spacecraft’s 6D pose parameters, or to localize pre-defined keypoints that are then used to compute pose through a Perspective-n-Point solver. We study an alternative solution that uses a convolutional network to predict keypoint locations, which are in turn used by a second network to infer the spacecraft’s 6D pose. This formulation retains the performance advantages of keypoint-based methods, while affording end-to-end training and faster processing. Our paper is the first to evaluate the applicability of such a method to the space domain. On the SPEED dataset, our approach achieves a mean rotation error of 4.69◦ and a mean translation error of 1.59% with a throughput of 31 fps. We show that computational complexity can be reduced at the cost of a minor loss in accuracy.
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Citations
Legrand, A., Renaud Detry, & De Vleeschouwer, C. (2022). End-to-end Neural Estimation of Spacecraft Pose with Intermediate Detection of Keypoints. Lecture Notes in Computer Science, 1(56), 757. https://doi.org/10.1007/978-3-031-19797-0 (Original work published 2022)