An approach to building a three-dimensional description of the environment of a robot using three cameras is presented. The main advantages of trinocular versus binocular stereo are simplicity, reliability, and accuracy. It is believed that these advantages make trinocular stereo vision of practical use for many robotics applications. The technique has been successfully applied to several indoor and industrial scenes. Experimental results are presented and discussed.>

We present recent advancements in our passive trinocular stereo system. These include a technique for calibrating and rectifying in a very eficient and simple manner the triplets of images taken for trinocular stereovision systerns. After the rectification of images, epipolar lines are parallel to the axes of the image coordinate frames. Therefore, potential matches between the three images satisfy simpler relations, allowing for a less complicated and more efficient matching algorithm. We also describe a more robust and general control strategy now employed in our trinocular stereo system. We have also developed an innovative method for the reconstruction of 3-D seginents which provides better results and a new validation technique based on the observation that neighbors in the image should be neighbors in space. Experiments are presented demonstrating these advancements. 2 Rectification of the images

We show in this article that when the environment is piecewise linear, it provides a powerful constraint on the kind of matches that exist between two images of the scene when the camera motion is unknown. For points and lines located in the same plane, the correspondence between the two cameras is a collineation. We show that the unknowns (the camera motion and the plane equation) can be recovered, in general, from an estimate of the matrix of this collineation. The two-fold ambiguity that remains can be removed by looking at a second plane, by taking a third view of the same plane, or by using a priori knowledge about the geometry of the plane being looked at. We then show how to combine the estimation of the matrix of collineation and the obtaining of point and line matches between the two images, by a strategy of Hypothesis Prediction and Testing guided by a Kalman filter. We finally show how our approach can be used to calibrate a system of cameras.