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Paper WeD310.3

WŁthrich, Manuel (EPFL), Pastor, Peter (University of Southern California), Righetti, Ludovic (University of Southern California), Billard, Aude (EPFL), Schaal, Stefan (University of Southern California)

Probabilistic Depth Image Registration incorporating Nonvisual Information

Scheduled for presentation during the Interactive Session "Interactive Session WeD-3" (WeD310), Wednesday, May 16, 2012, 17:30−18:00, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 18, 2018

Keywords Computer Vision for Robotics and Automation, Visual Tracking

Abstract

In this paper, we derive a novel registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which will play a central role in our algorithm. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL cite{rusu11} implementations of feature mapping and ICP, especially if nonvisual information is available.

 

 

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