RGB-D-Based Features for Recognition of Textureless Objects

@inproceedings{Thoduka2016RGBDBasedFF,
  title={RGB-D-Based Features for Recognition of Textureless Objects},
  author={Santosh Thoduka and Stepan Pazekha and Alexander Moriarty and Gerhard K. Kraetzschmar},
  booktitle={RoboCup},
  year={2016}
}
Autonomous industrial robots need to recognize objects robustly in cluttered environments. The use of RGB-D cameras has progressed research in 3D object recognition, but it is still a challenge for textureless objects. We propose a set of features, including the bounding box, mean circle fit and radial density distribution, that describe the size, shape and colour of objects. The features are extracted from point clouds of a set of objects and used to train an SVM classifier. Various… 
1 Citations
b-it-bots: Our Approach for Autonomous Robotics in Industrial Environments
TLDR
The b-it-bots team's combined 2D and 3D approach for object recognition has improved robustness and performance compared to previous years, and the task planning framework has moved away from large state machines for high-level control.

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