Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking

• URL: http://arxiv.org/abs/2106.08045v1
• Date: Tue, 15 Jun 2021 11:01:07 GMT
• Title: Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking
• Authors: Timon H\"ofer, Faranak Shamsafar, Nuri Benbarka and Andreas Zell
• Abstract summary: We propose a framework for pose estimation in highly cluttered scenes with small objects. In this work, we compare synthetic data generation approaches for object detection and pose estimation. We introduce a pose filtering algorithm that determines the most accurate estimated poses.
• Score: 14.076644545879939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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