Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
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by
Bowen Wen, Chaitanya Mitash, Kostas Bekris
2022
Abstract
Tracking the 6D pose of objects in video sequences is important for robot
manipulation. This work presents se(3)-TrackNet, a data-driven optimization
approach for long term, 6D pose tracking. It aims to identify the optimal
relative pose given the current RGB-D observation and a synthetic image
conditioned on the previous best estimate and the object's model. The key
contribution in this context is a novel neural network architecture, which
appropriately disentangles the feature encoding to help reduce domain shift,
and an effective 3D orientation representation via Lie Algebra. Consequently,
even when the network is trained solely with synthetic data can work
effectively over real images. Comprehensive experiments over multiple
benchmarks show se(3)-TrackNet achieves consistently robust estimates and
outperforms alternatives, even though they have been trained with real images.
The approach runs in real time at 90.9Hz. Code, data and supplementary video
for this project are available at
https://github.com/wenbowen123/iros20-6d-pose-tracking
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