3D pose estimation for robotic applications based on a multi-camera hybrid visual system

Abstract

An algorithm for the estimation of the position and orientation of a moving object using a hybrid eye-in-hand/eye-to-hand multi-camera system is presented. Based on the extended Kalman filter, this approach exploits the data provided by all the cameras without "a priori" discrimination, allowing real-time estimation. The proposed formulation can be used with different kinds of image features and different representations of the object orientation. A simulation case study is reported to test the feasibility and the effectiveness of the proposed technique