mlsanityData to 7 Oct 2026

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

arXiv 2302.11089 ↗First posted 22 Feb 2023cs.RO cs.AI cs.SYcited by 5

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Abstract (arXiv)

This review article is an attempt to survey all recent AI based techniques used to deal with major functions in 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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 142 of its 463 references that are in the map. First posted on arXiv 22 Feb 2023.

Builds on

142 of 463 references.

References · 142

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38 more
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50 most cited of 142 All 142 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2023The 7th AI City Challengecited by 20
  2. 2023Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directionscited by 20
  3. 2025From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networkscited by 1
  4. 2026PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platformscited by 1
  5. 2025Continual Learning for Image Captioning through Improved Image-Text Alignmentcited by 0