Dynamic integration of visual cues for position estimation

Three-dimensional (3D)position estimation using a single passive sensor, particularly vision, has frequently suffered from unreliability and has involved complex processing methods. Past research has combined vision with other active sensors in which the emphasis has been on data fusion. This paper attempts to integrate multiple passive 3D cues camera focus, camera vergence and stereo disparity using a single sensor. We argue that in the active vision paradigm an estimate of the position is obtained in the process of fixation in which the imaging parameters are dynamically controlled to direct the attention of the imaging system at the point of interest. Fixation involves integration of the passive cues in a mutually consistent way in order to overcome the deficiencies of any individual cue and to reduce the complexity of processing. Taking into account their reliabilities, the individual position estimates from the different cues are combined to form a final, overall estimate.