  Recent progress in object pose prediction provides a promising path for
robots to build object-level scene representations during navigation. However,
as we deploy a robot in novel environments, the out-of-distribution data can
degrade the prediction performance. To mitigate the domain gap, we can
potentially perform self-training in the target domain, using predictions on
robot-captured images as pseudo labels to fine-tune the object pose estimator.
Unfortunately, the pose predictions are typically outlier-corrupted, and it is
hard to quantify their uncertainties, which can result in low-quality
pseudo-labeled data. To address the problem, we propose a SLAM-supported
self-training method, leveraging robot understanding of the 3D scene geometry
to enhance the object pose inference performance. Combining the pose
predictions with robot odometry, we formulate and solve pose graph optimization
to refine the object pose estimates and make pseudo labels more consistent
across frames. We incorporate the pose prediction covariances as variables into
the optimization to automatically model their uncertainties. This automatic
covariance tuning (ACT) process can fit 6D pose prediction noise at the
component level, leading to higher-quality pseudo training data. We test our
method with the deep object pose estimator (DOPE) on the YCB video dataset and
in real robot experiments. It achieves respectively 34.3% and 17.8% accuracy
enhancements in pose prediction on the two tests. Our code is available at
https://github.com/520xyxyzq/slam-super-6d.
