Improving Pose Estimation Using Image, Sensor and Model Uncertainty

This work proposes a methodology for the analysis of the uncertainty in the localization of objects when considering uncertain image data, camera and object geometry parameters. The uncertainty is propagated through an extended static Kalman filter initialized with the parameters used for the localization and updated with new matched features obtained by back-projecting onto the image. At the end of the process, a better estimate of the object pose with its uncertainty is given along with a new estimate of the used uncertain object features and the camera parameters. The methodology is now in use in an object localization system.