Accurate trajectory estimation for unmanned aerial vehicles is considered a formidable challenge, especially in environments where GPS signals are unavailable. This thesis focuses on developing a bundle adjustment algorithm for the Lunar Nano Drone (LuNaDrone), an autonomous spacecraft designed to explore possible entrances to lunar pits and lava tubes. The work centers on implementing a bundle adjustment pipeline that fuses visual and inertial measurements to enhance trajectory accuracy. The LuNaDrone already incorporates an Extended Kalman Filter (EKF) for real-time navigation. However, these filters suffer from accumulated drift and errors when employed for long distances. This will lead to poor trajectory estimation especially in lunar environments where the conditions can be harsh and unpredictable.