Deep Learning and Visual SLAM for Autonomous Navigation of UAVs: Status, Challenges, and Future Perspectives

This paper discusses the application and integration of deep learning and visual SLAM technology in UAV autonomous navigation. With the wide application of UAVs in the fields of transportation, agriculture, military and environmental monitoring, improving the autonomous navigation capabilities of UAVs has become an important demand. This paper first analyses the advantages and disadvantages of deep learning and visual SLAM. Then, this paper emphasizes the core role of deep learning in feature extraction, target recognition and path planning. The key role of visual SLAM in real-time localization and environment mapping is highlighted as well. This paper discusses the combined application of these two technologies and demonstrates how to enhance the stability and accuracy of visual SLAM systems through deep learning in complex dynamic environments. Although the current technology still faces high computational requirements and real-time challenges, this paper proposes future research direction to promote the continued development of UAV autonomous navigation technology.