ICRA'09 Paper Abstract


Paper FrD2.2

Viksten, Fredrik (University of Linkoping), Forssen, Per-Erik (Linkoping University), Johansson, Bjorn (SICKIVP), Moe, Anders (SICKIVP)

Comparison of Local Image Descriptors for Full 6 Degree-Of-Freedom Pose Estimation

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation - V" (FrD2), Friday, May 15, 2009, 15:50−16:10, Room: ICR

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Computer Vision for Robotics and Automation, Visual Learning, Factory Automation


Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image descriptor. This paper examines how the performance for such a system varies with choice of local descriptor. This is done by comparing the performance of a full 6 degree-of-freedom pose estimation system for fourteen types of local descriptors. The evaluation is done on a database with photos of complex objects with simple and complex backgrounds and varying lighting conditions. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, and that affine covariant features do not work well in current pose estimation frameworks. The data sets and their ground truth is available on the web to allow future comparison with novel algorithms.



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