As part of DARPA's MARS2020 program, the Jet Propulsion Laboratory has developed a vision-based system for localization in urban environments that requires neither GPS nor active sensors. System hardware consists of a pair of small FireWire cameras and a standard Pentium-based computer. The inputs to the software system consist of: 1) a crude grid-based map describing the positions of buildings, 2) an initial estimate of robot location and 3) the video streams produced by the stereo pair. At each step during the traverse the system: captures new image data, finds image features hypothesized to lie on the outside of a building, computes the range to those features, determines an estimate of the robot's motion since the previous step and combines that data with the map to update a probabilistic representation of the robot's location. This probabilistic representation allows the system to simultaneously represent multiple possible locations. For our testing, we have derived the a priori map manually using non-orthorectified overhead imagery, although this process could be automated. The software system consists of three primary components. The first is a stereo-based visual odometry system that calculates the 6-degree of freedom camera motion between sequential frames. The second component uses a set of heuristics to identify straight-line segments that are likely to be part of a building exterior. Ranging to these straight-line features is computed using binocular or wide-baseline stereo. The resulting features and the associated range measurements are fed to the third software component, a particle-filter based localization system. This system uses the map and the most recent results from the first two to update the estimate of the robot's location. This report summarizes the design of both the hardware and software and describes the results of applying the system to the global localization of a camera system over an approximately half-kilometer traverse across JPL's Pasadena campus.