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Probabilistic relational scene representation and decision making under incomplete information for robotic manipulation tasks | IEEE Conference Publication | IEEE Xplore

Probabilistic relational scene representation and decision making under incomplete information for robotic manipulation tasks

Publisher: IEEE

Abstract:

In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target applicatio...View more

Abstract:

In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations.
Date of Conference: 31 May 2014 - 07 June 2014
Date Added to IEEE Xplore: 29 September 2014
Electronic ISBN:978-1-4799-3685-4
Print ISSN: 1050-4729
Publisher: IEEE
Conference Location: Hong Kong, China

References

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