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Bingham Procrustean Alignment for Object Detection in Clutter

2013-04-27 19:24:30
Jared Glover, Sanja Popovic
arXiv_CV
         
Abstract

A new system for object detection in cluttered RGB-D images is presented. Our main contribution is a new method called Bingham Procrustean Alignment (BPA) to align models with the scene. BPA uses point correspondences between oriented features to derive a probability distribution over possible model poses. The orientation component of this distribution, conditioned on the position, is shown to be a Bingham distribution. This result also applies to the classic problem of least-squares alignment of point sets, when point features are orientation-less, and gives a principled, probabilistic way to measure pose uncertainty in the rigid alignment problem. Our detection system leverages BPA to achieve more reliable object detections in clutter.

Abstract (translated)

URL

https://arxiv.org/abs/1304.7399

PDF

https://arxiv.org/pdf/1304.7399.pdf

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