Pose Clustering From Stereo Data

Abstract. This article describes an algorithm for pose or motion estimation based on clustering of parameters in the six-dimensional pose space. The parameter samples are computed from data samples randomly drawn from stereo data points. The estimator is global and robust, performing matches to parts of a scene without prior pose information. It is general, in that it does not require any particular object features. Empirical object models can be built largely automatically. An implemented application from the service robotic domain and a quantitative performance study on real data are presented.

• Type: Conference
• Year: 2008
• Where: VISAPP
• Authors: Ulrich Hillenbrand
• Keywords: Computer Vision, Prior Pose Information, Six-dimensional Pose Space, Stereo Data Points, VISAPP 2008