FGO Algorithms for Rover Navigation in Planetary Environment

Ensuring high-precision localization is one of the main challenges in planetary rover navigation, as GNSS-based positioning alone lacks the required accuracy for autonomous operations. This work investigates the application of Factor Graph Optimization (FGO) to improve localization accuracy by integrating GNSS data with an Inertial Measurement Unit (IMU). The motivation behind this research is to develop a more robust and precise localization framework by means of sensor fusion to improve navigation performance for planetary missions. The primary objective of this work is to assess the effectiveness of FGO in refining rover localization under different planetary conditions. Therefore, two optimization algorithms — Levenberg-Marquardt (LM) and Incremental Smoothing and Mapping (iSAM2) — were implemented using the GTSAM library. A remarkable feature of this approach is its adaptability, allowing modifications to key parameters such as the GNSS constellation orbital parameters, planetary radius, and gravity, thereby ensuring its applicability to a wide range of celestial bodies. To validate the proposed approach, simulations were conducted for both Earth and Moon environments. As a case study, the Moonlight initiative, an ESA program aimed at establishing a lunar navigation satellite system, was selected to highlight the growing importance of planetary navigation solutions. The results demonstrate that FGO significantly improves positioning and also provides moderate accuracy for attitude estimation, surpassing conventional GNSS-based methods in terms of effectiveness and consistency. This study contributes to the development of more reliable navigation techniques, offering a flexible and scalable solution for future lunar, Martian, and other space exploration missions as well. By showing the benefits of FGO-based sensor fusion, it helps to improve autonomous navigation accuracy in various planetary environments.