High Accuracy Model-Based Object Pose Estimation for Autonomous Recharging Applications
Abstract
This contribution describes a system for accurate, robust and fast six degrees-of-freedom object pose estimation based on multi-feature models and a recursive filtering approach in the context of autonomous vehicle recharging. Feature measurements are integrated sequentially to allow full control over the feature detection algorithms and the influence on the estimate. This makes the system able to cope with partial and short-term full object occlusions, Gaussian measurement noise as well as systematic model errors. For highly precise pose estimates, high resolution cameras are employed. Nevertheless, the proposed system achieves real-time performance while at the same time outperforming other algorithms in terms of accuracy and robustness.
Cite
Text
Jaspers et al. "High Accuracy Model-Based Object Pose Estimation for Autonomous Recharging Applications." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477635Markdown
[Jaspers et al. "High Accuracy Model-Based Object Pose Estimation for Autonomous Recharging Applications." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/jaspers2016wacv-high/) doi:10.1109/WACV.2016.7477635BibTeX
@inproceedings{jaspers2016wacv-high,
title = {{High Accuracy Model-Based Object Pose Estimation for Autonomous Recharging Applications}},
author = {Jaspers, Hanno and Mueller, Georg R. and Wuensche, Hans-Joachim},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-7},
doi = {10.1109/WACV.2016.7477635},
url = {https://mlanthology.org/wacv/2016/jaspers2016wacv-high/}
}