ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation
CoRR(2024)
摘要
In medical and industrial domains, providing guidance for assembly processes
can be critical to ensure efficiency and safety. Errors in assembly can lead to
significant consequences such as extended surgery times and prolonged
manufacturing or maintenance times in industry. Assembly scenarios can benefit
from in-situ augmented reality visualization, i.e., augmentations in close
proximity to the target object, to provide guidance, reduce assembly times, and
minimize errors. In order to enable in-situ visualization, 6D pose estimation
can be leveraged to identify the correct location for an augmentation. Existing
6D pose estimation techniques primarily focus on individual objects and static
captures. However, assembly scenarios have various dynamics, including
occlusion during assembly and dynamics in the appearance of assembly objects.
Existing work focus either on object detection combined with state detection,
or focus purely on the pose estimation. To address the challenges of 6D pose
estimation in combination with assembly state detection, our approach ASDF
builds upon the strengths of YOLOv8, a real-time capable object detection
framework. We extend this framework, refine the object pose, and fuse pose
knowledge with network-detected pose information. Utilizing our late fusion in
our Pose2State module results in refined 6D pose estimation and assembly state
detection. By combining both pose and state information, our Pose2State module
predicts the final assembly state with precision. The evaluation of our ASDF
dataset shows that our Pose2State module leads to an improved assembly state
detection and that the improvement of the assembly state further leads to a
more robust 6D pose estimation. Moreover, on the GBOT dataset, we outperform
the pure deep learning-based network and even outperform the hybrid and pure
tracking-based approaches.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要