## 5 Conclusions  
In this article, we presented a new space rover simulator and toolkit, achieving a high degree of rendered scene realism, ease of use through the adoption of a popular game engine, and familiar, industry-standard ROS interface compatibility, with integrated support for a wide range of heterogeneous computational hardware. The presented solution allows researchers to develop and test perception and control algorithms for autonomous lunar and exoplanetary rovers based on vision sensors. At its core, it is integrated with ROS 2 while providing superior visual fidelity when compared to other existing approaches. Two modes of operation allow for synthetic data generation and software integration testing. The configuration file can be used to modify the simulation parameters to meet specific usage scenarios, enabling automated testing across a range of parameters, along with easy experiment versioning and replication. Integration with ROS 2 allows for testing a rich set of existing algorithms in exploration scenarios involving broadly understood perception, navigation, and motion planning, using the newest methods based on deep learning.  
The simulator displayed favourable performance, in terms of generated image fidelity. It achieved results that surpassed those achieved with the most popular synthetic lunar rock dataset in the transfer learning experiment, in which the test set consisted of real lunar and analogue mission data, confirming its usefulness using objective evaluation. The simulator-generated data were also successfully used for evaluating the object detection and visual odometry algorithms. All the experiments were performed using a range of various computational platforms, including conventional microcomputers and embedded devices with GPU AI accelerators, as well as complex system-on-chip devices with programmable hardware. This confirmed the simulator's usefulness, in terms of testing using real mission hardware. The simulator is flexible enough to accommodate other devices.  
The solution consists of modules based on standardised components and, as such, has very good potential for expansion, in terms of the range of simulated sensors, environments, and other functions or integration as a part of more complex test setups. Overall, the presented simulator significantly extends the capabilities for replicating critical components of space robotics applications in perception and autonomy. The simulator can easily be extended, due to its modular nature, providing support for other hardware, sensors,  
Figure 7: Trajectory of rover calculated by visual odometry (**top**) and its visual comparison to ground truth (**bottom**).  
and terrestrial and extraterrestrial environments. The visual fidelity can also further be extended by using more realistic textures and object models, as well as by the application of domain transfer methods [59]. The authors hope that the presented suite of software will pave the way for further advancements in the field of space robotics and beyond.  
Conceptualisation, D.P., B.P. and M.K.; methodology, D.P., B.P. and M.K.; software, D.P. and B.P.; validation, D.P. and B.P.; formal analysis, D.P. and B.P.; investigation, D.P. and B.P.; writing--original draft preparation, D.P., B.P., M.K. and P.D.; writing--review and editing, D.P., B.P., M.K. and P.D.; visualisation, D.P. and B.P.; supervision, M.K. and P.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Polish Ministry of Education and Science under Grant 0214/SBAD/0244.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/PUTvision/LunarSim](https://github.com/PUTvision/LunarSim)]([https://github.com/PUTvision/LunarSim](https://github.com/PUTvision/LunarSim)) (accessed on 15 November 2023).  
The authors declare no conflict of interest.