# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 7 Conclusions and Future Work  
In general, the typical algorithms employed in ORB-SLAM3, NavMesh generation, and U-map creation are referenced in [20], [5], and [30], respectively. ORB-SLAM3 computes visual odometry and, at a broader scale, includes loop detection within the SLAM framework. The Atlas map is a powerful tool for real-time positioning of robot agents, allowing for accurate navigation. Using ORB-SLAM3, the SurfelMeshing algorithm reconstructs a 3D environment based on the visual odometry. Once we have a 3D mesh that simulates the real-world environment, we convert this mesh into a NavMesh. The above process generates two types of maps: the Atlas map and the NavMesh. NavMesh is preferred for navigation instead of the original mesh because its data structure defines walkable areas. The Watershed Algorithm, applied through Recast Navigation, generates non-overlapping regions, making the walkable paths continuous. In contrast, the original 3D meshes are discontinuous, which complicates navigation. Once we have both the Atlas and NavMesh, we can begin the navigation process. This includes two key tasks: path planning and obstacle avoidance. For path planning, we use the A* pathfinding algorithm on the NavMesh. A* is efficient in terms of computational time, especially when the NavMesh is optimized to define walkable areas. As shown in Tables 4 and 5, the A* algorithm can compute the global path at real-world scales within a very short time. Furthermore, if the map contains mostly empty spaces with few complex objects, the number of nodes in the walkable areas can be minimized, further improving efficiency in our navigation process. Regarding obstacle avoidance, the NavMesh is reconstructed whenever a dynamic obstacle is detected. Our obstacle detection method relies on U-V disparity maps, as detailed in [31]. The U-map is created by accumulating the disparity values along the columns of the image, while the V-map accumulates along the rows. Obstacles are typically characterized by a small number of disparity values, which are clearly visible in the U-map. In this map, all disparity values for a single object fall within the same range because the distance from the camera to the object remains constant. Finally, the real-time performance of NavMesh generation has been validated on the commercial platform Unity 3D, where we also apply our NavMesh reconstruction using the same Recast Navigation system [5]. By detecting obstacles through the U-map, we can update the walkable areas and reconstruct the NavMesh in real time to accommodate dynamic changes in the environment. The innovative 3D vision-based autonomous navigation system introduced in this research presents promising applications across a wide range of industries:  
* Wide-Range Outdoor Navigation: The system offers transformative potential for autonomous vehicles, including self-driving cars, delivery robots, and other automated transport solutions. By enabling efficient, safe, and adaptable navigation in complex environments, it contributes significantly to advancing autonomous transportation. Furthermore, the research facilitates outdoor navigation in real-world, large-scale scenarios covered by wide-range GPS or networking. Leveraging services such as Google Maps for mesh data, the system can navigate complex urban environments with ease. For instance, Figure 17 illustrates mesh data depicting the Jianguo Express-way crossing several surface streets, while Figure 18 demonstrates a yellow navigation path displayed on NavMeshes, enabling movement from a lower-level street to the elevated Jianguo Expressway. * Search and Rescue, Construction, and Disaster Response: The system's adaptability to various terrains, including uneven surfaces and wheelchair ramps, makes it invaluable for challenging scenarios such as search and rescue missions, construction sites, and disaster response. Its ability to perform real-time navigation in complex environments addresses critical needs in these contexts.