Estimation of the absolute camera pose for environment recognition of industrial robotics

Abstract

The problem of estimating and predicting the absolute camera pose (the position and orientation of a camera with respect to the world coordinate system) is approached by fusion of measurements from inertial sensors (accelerometers and gyroscopes) and robot control system. The sensor fusion approach described in this paper is based on non-linear filtering of multi-rate extended Kalman filter. In this way, camera pose estimates, with improved accuracy and sampling rate as well as reduced computation complexity, are available. Experiments that an industrial robot moves the sensors (camera and inertial measurement unit) in an indoor-global positioning system (GPS)-based global referencing system are presented. The absolute camera pose, provided by indoor-GPS, allows for a performance evaluation. The experimental results confirm also the dynamics improvement of the estimated absolute camera pose.