The MoonRanger Lunar rover will demonstrate continuous, on-board, long-range autonomous navigation at the Moon's South Pole. In this thesis, we describe how MoonRanger estimates its position and orientation using several sensors: an Inertial Measurement Unit (IMU), Sun Sensor, wheel encoders, and cameras. We thoroughly review the state estimation approaches for space vehicles. Then, we introduce MoonRanger's state estimator, which performs visual-wheel-inertial odometry, the core of which is an attitude estimator. We derive the state estimator using detailed sensor models, accounting for many error sources in the measurements. Next, we develop a custom simulation infrastructure, which enables rapid iteration, testing, and Monte Carlo analyses of the algorithms. We use this simulator to validate the estimator's design through ablation studies. Lastly, we prove the estimator's effectiveness through a field test.

We cover several implementation details important to making the software computationally efficient, memory-safe, debug-able, and robust to uncertainties. In the appendices, we provide a thorough background of 1) the mathematical fundamentals of state estimation, 2) IMU noise characterization and 3) an in-field method for calibrating an IMU's pitch to reduce elevation drift. These algorithms, analyses, and implementation details can serve as a guide to developing state estimators for future planetary rovers.