# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 7 Conclusions and Future Work
However, further investigation is required to evaluate its robustness under sensor noise, localization drift, and rapid environmental changes, which may affect its performance in highly dynamic scenarios. Future work will focus on assessing the system's resilience to external disturbances, including varying lighting conditions, sensor occlusions, and mechanical wear, ensuring its reliability in real-world deployments. * Virtual and Augmented Reality: The precise 3D mapping capabilities of the system extend its applications to virtual and augmented reality, facilitating the creation of immersive environments and enhancing augmented reality experiences. This connection between 3D perception and interactive applications further broadens the system's potential impact beyond robotics. However, optimizing computational efficiency and real-time processing will be a key area of improvement, as faster and more efficient navigation is essential for both robotics and VR/AR applications. Figure 17: Mesh reconstruction downloaded from Google Maps. This study makes significant contributions to the field of autonomous navigation for wheeled mobile robots. By presenting a real-time pipeline for 3D reconstruction using RGB-D cameras and implementing pathfinding algorithms in 3D space, we have advanced the state of the art in mobile robotics. The successful demonstration of robot navigation through navigation meshes (NavMeshes) offers a practical solution for interpretable 3D map-based navigation. Additionally, the integration of an RGB-D sensor enables moving object detection, ensuring effective collision avoidance. These advancements have the potential to substantially enhance the performance and reliability of autonomous vehicles and robotic systems. Despite these achievements, several areas warrant further investigation and refinement. Future work will focus on expanding the evaluation metrics beyond navigation accuracy, incorporating energy efficiency, processing speed, and adaptability to different robot models to assess the system's broader applicability. Additionally, while the current study primarily targets wheeled mobile robots, extending the system to other platforms, such as legged robots, will be an important direction to explore, requiring adaptations to different locomotion models. By addressing these aspects, we aim to enhance the system's robustness and efficiency, broadening its applicability across various autonomous navigation scenarios. Conceptualization, T.-W.W. and H.-P.H. ; Funding acquisition, H.-P.H. ; Methodology, T.-W.W. and Y.-L.Z. ; Project administration, H.-P.H. ; Resources, H.-P.H. ; Software, T.-W.W. and Y.-L.Z. ; Supervision, H.-P.H. ; Validation, T.-W.W. and Y.-L.Z. ; Visualization, T.-W.W.; Writing--original draft, T.-W.W. and Y.-L.Z. ; Writing--review and editing, T.-W.W., H.-P.H. and Y.-L.Z. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Science and Technology Council, Taiwan, under NSTC 112-2221-E-002-149-MY3. Not applicable. The raw data supporting the conclusions of this article will be made available by the corresponding author upon request. This research is an extended version of our previous work presented at the 2024 National Conference on Mechanical Engineering of CSME [32]. The authors declare no conflicts of interest.