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：本文献综述全面概述了用于自主导航的端到端深度学习框架，包括障碍物检测、场景感知、路径规划与控制。通过分析近期研究并评估深度学习方法的实施与测试，本文旨在弥合自主导航与深度学习之间的鸿沟。研究强调了导航对移动机器人、自动驾驶车辆及无人航空器的重要性，同时指出因环境复杂性、不确定性、障碍物、动态环境以及多智能体路径规划需求所带来的挑战。综述揭示了深度学习在工程数据科学中的快速发展及其对创新导航方法的推动作用，讨论了该领域相关的最新跨学科工作，并简要展望了深度学习方法在自主导航中的局限性、挑战及潜在发展领域。最后，本文总结了不同阶段的研究发现与实践，关联了现有与未来方法及其适用性、可扩展性与局限性。本综述为从事自主导航与深度学习领域的研究人员及实践者提供了宝贵资源。