Integrating UAS for 3D Terrain Mapping and Autonomous Navigation: A Review of Multi-Camera and Reinforcement Learning

This paper explores the integration of Unmanned Aerial Systems (UAS) for 3D terrain mapping and autonomous navigation, emphasizing the fusion of advanced technologies such as multi-camera setups and reinforcement learning (RL). As UAS applications expand across sectors like urban planning, disaster management, agriculture, and environmental conservation, the need for sophisticated mapping techniques and autonomous navigation systems grows. UAS equipped with high-performance sensors can create detailed three-dimensional terrain models, facilitating real-time data analysis for critical applications. Furthermore, RL enhances UAS autonomy by enabling the learning of optimal navigation strategies through environmental interaction. This study addresses key challenges, including ethical concerns, regulatory frameworks, and the reliability of autonomous systems, while highlighting future directions in algorithm development, sensor integration, traffic management, and real-world testing to ensure the effective implementation of UAS technology in various industries.