This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

翻译：本审查文件全面概述了自主航行中使用的端到端深学习框架,包括障碍探测、现场感知、路径规划和控制,目的是通过分析最近的研究,评价深层学习方法的实施和测试,弥合自主导航与深层学习之间的差距,强调流动机器人、自主飞行器和无人驾驶飞行器的导航的重要性,同时也承认环境复杂性、不确定性、障碍、动态环境以及规划多物剂路径的必要性带来的挑战。本审查着重介绍了工程数据科学及其创新导航方法开发方面深层学习的迅速增长,讨论了与该领域有关的近期跨学科工作,并简要介绍了自主航行深层学习方法的局限性、挑战和潜在增长领域。最后,本文件总结了不同阶段的调查结果和做法,将现有和未来方法、其适用性、可扩展性和局限性联系起来。审查为自主导航和深层学习领域的研究人员和从业人员提供了宝贵的资源。</s>