Probabilistic models for robot-based object segmentation

Daniel Beale; Pejman Iravani; Peter Hall

doi:10.1016/j.robot.2011.08.003

Probabilistic models for robot-based object segmentation

Daniel Beale, Pejman Iravani, Peter Hall

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Abstract

This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robot's motion to determine the shape and location of objects. This allows a robot with no prior knowledge of its workspace to isolate objects against their surroundings by moving them and observing their visual feedback. The main contribution of the paper is to improve upon current methods by allowing object segmentation in changing environments and moving backgrounds. The approach allows optimal values for the algorithm parameters to be estimated. Empirical studies against alternatives demonstrate clear improvements in both planar and three dimensional motion.

Original language	English
Pages (from-to)	1080-1089
Number of pages	10
Journal	Robotics and Autonomous Systems
Volume	59
Issue number	12
Early online date	16 Aug 2011
DOIs	https://doi.org/10.1016/j.robot.2011.08.003
Publication status	Published - Dec 2011

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10.1016/j.robot.2011.08.003

Beale_RAS_2011.pdf
NOTICE: this is the author’s version of a work that was accepted for publication in Robotics and Autonomous Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Autonomous Systems, Vol 59, Issue 12, 2011, DOI 10.1016/j.robot.2011.08.003
Accepted author manuscript, 9.65 MB

Cite this

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Probabilistic models for robot-based object segmentation

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