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

Rasoul Mojtahedzadeh, Abdelbaki Bouguerra, Erik Schaffernicht, Achim J. Lilienthal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5685-5690
Number of pages6
ISBN (Electronic)9781479936854, 9781479936854
DOIs
StatePublished - 22 Sep 2014
Externally publishedYes
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: 31 May 20147 Jun 2014

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Country/TerritoryChina
CityHong Kong
Period31/05/147/06/14

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