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Robust 6D Object Pose Estimation in Cluttered Scenes

1,058 views · Published 13 August 2018 · 1:35 · Indexed 5 October 2026

Channel: nimbro · 2018 · Science & Technology

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Video attachement  for:
Arul Selvam Periyasamy, Max Schwarz, and Sven Behnke:
"Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networks"
Accepted for 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.
http://www.ais.uni-bonn.de/papers/IROS_2018_Periyasamy.pdf

Abstract: 
Object pose estimation is a crucial prerequisite for robots to perform autonomous manipulation in clutter.
Real-world bin-picking settings such as warehouses present additional challenges, e.g., new objects are added constantly.
Most of the existing object pose estimation methods assume that 3D models of the objects is available beforehand. 
We present a pipeline that requires minimal human intervention and circumvents the reliance on the availability of 3D models by a fast data acquisition method and a synthetic data generation procedure.
This work builds on previous work on semantic segmentation of cluttered bin-picking scenes to isolate individual objects in clutter. An additional network is trained on synthetic scenes to estimate object poses from a cropped object-centered encoding extracted from the segmentation results. 
The proposed method is evaluated on a synthetic validation dataset and cluttered realworld scenes.

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