A binocular-stereo-matching algorithm for making rapid visual range measurements in noisy images is described. This technique is developed for application to problems in robotics where noise tolerance, reliability and speed are predominant issues. A high speed pipelined convolver for preprocessing images and an 'unstructured light' technique for improving signal quality are introduced to help enhance performance to meet the demands of this task domain. These optimizations, however, are not sufficient. A closer examination of the problems encountered suggests that broader interpretations of both the objective of binocular stereo and of the zero-crossing theory of Marr and Poggio are required. In this paper, we restrict ourselves to the problem of making a single primitive surface measurement. For example, to determine whether or not a specified volume of space is occupied, to measure the range to a surface at an indicated image location, or to determine the elevation gradient at that position. In this framework, we make a subtle but important shift from the explicit use of zero-crossing contours (in band-pass filtered images) as the elements matched between left and right images, to the use of the signs between zero-crossings. With this change, we obtain a simpler algorithm with a reduced sensitivity to noise and a more predictable behavior. The PRISM system incorporates this algorithm with the unstructured light technique and a high speed digital convolver. It has been used successfully by others as a sensor in a path planning system and a bin picking system.