3D Object Finding Using Geometrical Constraints on Depth Images

Abstract

Finding an object in a 3D scene is an important problem in the robotics, especially in assistive systems for visually impaired people. In most systems, the first and most important step is how to detect an object in a complex environment. In this paper, we propose a method for finding an object using geometrical constraints on depth images from a Kinect. The main advantage of the approach is it is invariant to lighting condition, color and texture of the objects. Our approach does not require a training phase, therefore it can reduce the time of preparing data and learning model. The objects of interest have a simple geometrical structure such as coffee mugs, bowls, boxes and are on a table. Overall, our approach is faster and more accurate than methods using 2D features on depth images for training an object model.