A novel system for object pose estimation using fused vision and inertial data

Abstract

Six-degree-of-freedom (6-DoF) pose estimation is of fundamental importance to many applications, such as robotics, indoor tracking and Augmented Reality. Although a number of pose estimation solutions have been proposed, it remains a critical challenge to provide a low-cost, real-time, accurate and easy-to-deploy solution. Addressing this issue, this paper describes a multisensor system for accurate pose estimation that relies on low-cost technologies, in particular on a combination of webcams, inertial sensors and a printable colored fiducial. With the aid of inertial sensors, the system can estimate full pose both with monocular and stereo vision. The system error propagation is analyzed and validated by simulations and experimental tests. Our error analysis and experimental data demonstrate that the proposed system has great potential in practical applications, as it achieves high accuracy (in the order of centimeters for the position estimation and few degrees for the orientation estimation) using the mentioned low-cost sensors, while satisfying tight real-time requirements.