## 2 Related Work  
The potential of simulation as a tool for aiding the development of space robots has long been recognised, with examples such as SpaceDyn [20], ROAMS [21], and 3 DROV [22] paving the way for future, more advanced solutions. In their survey [23], [PERSON] et al. gave an overview of simulators, almost a decade later. They noted the emergence of follow-up solutions, such as ROSTDyn [24] or the use of commercial tools for multibody simulation [25] integrated within more extensive toolsets [26]. A large part of the functionality of both of the aforementioned simulators is focused on accurately modelling the physical properties of the rover and the environment, such as rover-surface soil interactions, slip dynamics, thermal models, power system models, and characteristics of the rover's frame, sensors, and actuators. The gathered information is very valuable: high-fidelity wheel-soil interaction and rover mobility simulations across various conditions have been crucial for developing successful motion control algorithms, facilitating extraterrestrial exploration in multiple missions.  
With the first steps already taken, it was evident that future missions would require increased situational awareness and autonomy, including VO and SLAM. The feasibility of such an approach was proven in large-scale analogue missions [27]. Analogue missions are considered the most valuable source of information, with more recent examples being [28; 29]. However, gathering real-world data is a time-consuming, laborious process, which is limited, in terms of the number of scenarios and control over physical world parameters. Aside from SLAM and VO, recent advances in machine learning for visual information processing have created the need for high-fidelity visual information generation in large volumes, preferably with automatically generated and associated ground truth. The ability to use widely adopted, standard software for control and broad hardware support is also welcome.  
A game-engine-based simulator introduced by NASA [30] offers excellent visual fidelity in replicating lunar surface images, but focuses primarily on topography design tools, which lack machine learning support components. It is, however, compatible with an in-house tricklibrary for experiment simulation control and HIL capability. With the advent of Space ROS [15], compatibility with the ROS framework will be a significant advantage for robot rover simulators. The Gazebo simulator, typically paired with ROS [31], has been used successfully in various applications and provides seamless integration, but it was not designed with visual fidelity or machine learning support in mind. The research presented in [32] used Gazebo with custom extensions, to improve the image quality of the rendered scene. However, achieving an acceptable level of visual fidelity and performance required extensive modifications to the rendering engine, which were proprietary. The re cently proposed Martian terrain simulators [33; 34] also used ROS and Gazebo, but were mostly directed at the realistic simulation of the rover and soil contacts and terra-dynamics. Modern simulation systems enable automated training data generation and integration with ROS: for example, Nvidia Isaac Sim [35], which enables accurate simulation with realistic visuals and ROS support. However, it lacks space environments (the existing environments are primarily focused on robotic manipulation and robot warehouses) and is a partially closed, company-developed solution that requires specific hardware and infrastructure. It is important to note that none of these simulators are specifically designed for the development of computer vision algorithms in the lunar environment.  
Illustrative frames extracted from our simulator, juxtaposed with sample images generated by prior solutions, are shown in Figure 2.