Motion estimation from stereo imagery, sometimes called visual odometry, is a well-known process. However, it is difficult to achieve good performance using standard techniques. We present the results of several years of work on an integrated system to localize a mobile robot in rough outdoor terrain using visual odometry, with an increasing degree of precision. We discuss issues that are important for real-time, high-precision performance: choice of features, matching strategies, incremental bundle adjustment, and filtering with inertial measurement sensors. Using data with ground truth from an RTK GPS system, we show experimentally that our algorithms can track motion, in off-road terrain, over distances of 10 km, with an error of less than 10 m (0.1%).