<title>Structured-lighting approach to enhance pose characterization using global image descriptors for a model-free robot positioning problem</title>

Abstract

Pose characterization refers to the unique representation of an object's pose through a set of image features. In this regard, one of the prerequisite for using visual images in representing an object's pose is a set of view-dependent image features that is unique to a single position and orientation of the object relative to the camera. Here, we propose an enhancement to the global feature extraction method. It utilizes an active illumination that projects a set of orthogonal grid patterns on the object. The deformations of the grid pattern incident on the complex target object carried important information about the surface structure of the target. This grid-encoded image can be characterized using global descriptors. Results indicate that this illumination increases the sensitivity of the global features to changes in object pose.