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Visual odometry can augment or replace wheel odometry when navigating in high slip terrain which is quite important for autonomous navigation on Mars. We present a computationally efficient and robust visual odometry algorithm developed for the Mars Science Laboratory mission. This algorithm is a significant improvement over the algorithm developed for the Mars Exploration Rover Mission because it is at least four time more computationally efficient and it tracks significantly more features. The core of the algorithm is an integrated motion estimation and stereo feature tracking loop that allows for feature recovery while guiding feature correlation search to minimize computation. Results on thousands of terrestrial and Martian stereo pairs show that the algorithm can operate with no initial motion estimate while still obtaining subpixel attitude estimation performance.