Current rover localization techniques such as visual odometry have proven to be very effective on short- to medium-length traverses (e.g., up to a few kilometers). This paper deals with the problem of long-range rover localization (e.g., 10 km and up) by developing an algorithm named MOGA (Multi-frame Odometry-compensated Global Alignment). This algorithm is designed to globally localize a rover by matching features detected from a three-dimensional (3D) orbital elevation map to features from rover-based, 3D LIDAR scans. The accuracy and efficiency of MOGA are enhanced with visual odometry and inclinometer/sun-sensor orientation measurements. The methodology was tested with real data, including 37 LIDAR scans of terrain from a Mars–Moon analog site on Devon Island, Nunavut. When a scan contained a sufficient number of good topographic features, localization produced position errors of no more than 100 m, of which most were less than 50 m and some even as low as a few meters. Results were compared to and shown to outperform VIPER, a competing global localization algorithm that was given the same initial conditions as MOGA. On a 10-km traverse, MOGA's localization estimates were shown to significantly outperform visual odometry estimates. This paper shows how the developed algorithm can be used to accurately and autonomously localize a rover over long-range traverses. © 2010 Wiley Periodicals, Inc.