> For the complete documentation index, see [llms.txt](https://2025.istvs.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://2025.istvs.org/submissions/papers/6905.md). # 6905 - INVESTIGATING DATA COLLECTION AND PROCESSING STRATEGIES FOR UAV-BASED TERRAIN MODELS IN VEHICLE DYNAMICS SIMULATION {% hint style="info" %} Paper presented at ISTVS 2025 | 55th Conference of the International Society for Terrain-Vehicle Systems {% endhint %} **Authors:** *Herman Hamersma, Christian Van Aswegen, Glenn Guthrie, Schalk Els, Carl Becker* **Keywords:** Terrain modelling; road profiling; off-road mobility **Abstract:** Accurate modelling of the interaction between a tyre and the terrain is essential for effective vehicle dynamics simulation, especially in challenging off-road conditions. A previous study outlined the development of three-dimensional terrain models using images captured by an unmanned aerial vehicle (UAV) and suggested several avenues for further investigation. This study addresses those recommendations, which include: (1) examining the impact of various image collection strategies, such as different image overlap ratios, varying flight path altitudes, and using both orthographic and oblique images, and (2) expanding the previous results to encompass a broader range of terrains, including a comparison of experimental measurements from a test vehicle with simulation results. Additionally, the interpolation technique used to regularise the point cloud data was found to significantly affect the terrain model. Images were collected across multiple test tracks at a proving ground for which baseline measurements from a traditional mechanical profilometer were available for direct comparison with the UAV-obtained data. The findings reveal that while constructing three-dimensional terrain models from UAV data is straightforward, several potential pitfalls must be carefully avoided, as they can significantly affect the accuracy of the results. The results provide a step-by-step guide to developing terrain models of various surfaces, from relatively smooth man-made roads (e.g., an ISO 8608:2016 Class-C road) to very rough natural terrain, opening various avenues for future mobility research in challenging terrains. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://2025.istvs.org/submissions/papers/6905.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.