Abstract:
The MoonRanger Lunar rover will demonstrate continuous, on-board, long-range autonomous navigation at the Moon’s South Pole. In this thesis, I describe how MoonRanger estimates its position and orientation using input from several sensors: Inertial Measurement Unit (IMU), Sun Sensor, wheel encoders, and cameras. I thoroughly review the state estimation approaches for prior space rovers and rotorcraft. MoonRanger’s state estimator performs visual-wheel-inertial odometry, the core of which is an attitude estimator. I derive the state estimator using detailed sensor models, accounting for and modeling the many error sources corrupting the sensors’ measurements. Next, I describe my custom simulation infrastructure, which enables rapid iteration, testing, and Monte Carlo analyses of the algorithms. I use this simulator to validate my design choices through ablation studies. Lastly, I prove the estimator’s effectiveness through physical tests.

I cover several implementation details important for making the software computationally efficient, memory-safe, debug-able, and robust to uncertainties. I provide a thorough background of 1) the mathematical fundamentals for 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 flight rovers on the Moon and beyond.

Committee:

David Wettergreen
Red Whittaker
Michael Kaess
Easton Potokar