Optimizing Visual Navigation and 3D Reconstruction in Unmapped Environments Using Deep Reinforcement Learning and Multi-Camera Systems: A Comprehensive Review

Abstract

This comprehensive review examines advanced methodologies for optimizing visual navigation and 3D reconstruction in unmapped environments using deep reinforcement learning and multi-camera systems. The focus is on addressing the challenges faced in dynamic and unpredictable contexts, particularly through the integration of deep reinforcement learning techniques and multi-camera systems. As advancements in these technologies proliferate, their potential impact across industries such as robotics, autonomous vehicles, and telepresence is significant. The review summarizes the evolution of 3D reconstruction techniques, the application of deep reinforcement learning in navigation strategies, and the importance of multi-camera systems in creating comprehensive spatial representations, thereby providing a theoretical foundation and practical guidance for achieving efficient autonomous navigation and environmental mapping.