**4. Conclusions and Future Works**

This research proposed a rock segmentation framework for the navigation vision of the planetary rovers using the synthetic algorithm and transfer learning. This framework provided an end-to-end rock segmentation solution for the future planetary rover autonomy. Furthermore, the proposed synthetic algorithm provided a new idea for handling the challenge of the lack of pixel-level semantic annotations in the planetary explorations. The synthetic dataset also provided a valid dataset and benchmark for the related research. The proposed NI-U-Net++ achieved the best results (see Section 3.2) in all three popular metrics compared to the state-of-the-art (the accuracy, IoU, Dice score, and RMSE are 99.41%, 0.8991, 0.9459, and 0.0075, respectively). Moreover, both the pre-training and transfer-training processes achieved outstanding training curves and results (the accuracy, IoU, Dice score, and RMSE are 99.58%, 0.7476, 0.8556, and 0.0557, respectively), which proved the assumptions (of the proposed synthetic algorithm) in Section 2.2.

The proposed framework made a significant step in the semantic segmentation of unstructured planetary explorations. As a cheap and extensive sensor, the monocular camera generates a large amount of data for planetary rover navigation. The proposed framework can efficiently conduct a semantic analysis for the planetary rover. These rocks can be integrated into the visual navigation system to further assist various advanced functions, such as path planning, localization, scene matching, etc.

The future works include transfering the proposed framework to the onboard device. The proposed framework uses the normal TensorFlow library, while only TensorFlow lite can operate on the onboard device. The potential action may also include the network slimming to fit the specific onboard device. Furthermore, the proposed NI-U-Net++ requires optimizations for the targeted system, hardware, and software.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/math9233048/s1, Video S1: The demo video of the proposed rock segmentation solution.

**Author Contributions:** Conceptualization, B.K.; methodology, B.K.; software, B.K. and M.W.; validation, B.K.; investigation, B.K., Z.A.R. and Y.Z.; resources, Z.A.R.; writing—original draft preparation, B.K.; writing—review and editing, B.K., M.W., Z.A.R. and Y.Z.; visualization, B.K., M.W., Z.A.R. and Y.Z.; supervision, Z.A.R. and Y.Z.; project administration, Z.A.R.; funding acquisition, Z.A.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** The Future Aviation Security Solutions (FASS) Programme, a joint Department for Transport and Home Office initiative with support from Connected Places Catapult (CPC) part funded this research.

**Data Availability Statement:** The proposed dataset is openly available in Cranfield Online Research Data (CORD) at https://doi.org/10.17862/cranfield.rd.16958728. The Katwijk beach planetary rover dataset is available at https://robotics.estec.esa.int/datasets/katwijk-beach-11-2015/.

**Acknowledgments:** All simulations have been carried out using the HPC facility (HILDA) at Digital Aviation Research and Technology Centre (DARTeC), Cranfield.

**Conflicts of Interest:** The authors declare no conflict of interest.
