Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking
This addresses the problem of inaccurate depth sensors for small objects in industrial robotics, but it appears incremental as it builds on existing methods with refinements.
The paper tackles 6D pose estimation for small objects in cluttered bin picking by proposing a framework that primarily uses RGB data and refines with depth, comparing synthetic data generation methods and introducing a pose filtering algorithm to improve accuracy.
Bin picking is a core problem in industrial environments and robotics, with its main module as 6D pose estimation. However, industrial depth sensors have a lack of accuracy when it comes to small objects. Therefore, we propose a framework for pose estimation in highly cluttered scenes with small objects, which mainly relies on RGB data and makes use of depth information only for pose refinement. In this work, we compare synthetic data generation approaches for object detection and pose estimation and introduce a pose filtering algorithm that determines the most accurate estimated poses. We will make our