Evaluating 6D Pose Estimation for Robotic Automation in Automotive Internal Logistics

The core message of this article is that while recent advances in 6D pose estimation using neural networks show promise for automating automotive internal logistics tasks, current state-of-the-art approaches do not yet meet the stringent industry requirements in terms of robustness and reliability, particularly due to the lack of reliable uncertainty estimation in the pose predictions.