Integration of Inertial Information with Vision towards Robot Autonomy

Reconstructing 3D data from images acquired by cameras is a difficult task. The problem becomes harder if the goal is to recover the dynamics of the 3D world from the image flow. However, it is known that humans integrate and combine the information from different sensorial systems to perceive the world. For example, tlie human vision system has close links with the wstibular system to perform everyday tasks. A computational approach for sensorial data integration, inertial and vision, is presented for a mobile robot equipped with an active vision system and inertial sensors. The inertial information is a different sensorial modality and, in this article, we explain our initial steps to combine this information with other sensorial systems, namely vision. Some of the benefits of using inertial information for navigation ani1 dynamic visual processing are described in the article. During tile development of these studies a low-cost inertial system prototype was developed. A brief description of low-cost inertial sensors and their integration in an inertial system prototype is also described. The set of sensors used in the prototype include three piewelectric vilmating gyroscopes, a tri-axial capacitive accelerometer and a dual axis clinometer. As a first approach the clinometer is used to track camera's pan and tilt, relative to a plane normal to the gravity vector and parallel to tlie ground floor. This provides the orientation data that, combined with a proccss of visual fixation, enables the identification of the ground plane or others parallel to it. An algorithm that segments the image, identifying the floor along which the vehicle can move is thus obtained.