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

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dc.contributor.authorBaston, George
dc.date.accessioned2024-12-12T08:02:15Z
dc.date.available2024-12-12T08:02:15Z
dc.date.issued2024-10-17
dc.description.abstractThis 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.
dc.identifier.citationNot applicable
dc.identifier.urihttps://africarxiv.ubuntunet.net/handle/1/1696
dc.language.isoen_US
dc.publisherNot applicable
dc.relation.ispartofseriesNot applicable; Not applicable
dc.subjectVisual Navigation
dc.subject3D Reconstruction
dc.subjectDeep Reinforcement Learning
dc.subjectMulti-Camera Systems
dc.subjectAutonomous Systems.
dc.titleOptimizing Visual Navigation and 3D Reconstruction in Unmapped Environments Using Deep Reinforcement Learning and Multi-Camera Systems: A Comprehensive Review
dc.title.alternativeDeep Reinforcement Learning and Multi-Camera Integration for Enhanced 3D Reconstruction and Visual Navigation in Uncharted Territories
dc.typePreprint

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