Path Planning Based on SLAM Technology and Deep Learning

DOI

10.2991/978-94-6463-823-3_42How to use a DOI?

Keywords

Simultaneous Localization and Mapping; Deep learning; Path Planning

Abstract

In the field of autonomous robotics, path planning has been a critical component, enabling robots to navigate efficiently and safely in complex environments. Simultaneous Localization and Mapping (SLAM) technology plays a foundational role by enabling robots to build a map of their environments while simultaneously tracking their location inside it. Traditional path planning methods, although effective in structured settings, often struggle with uncertainties and dynamic changes. In recent years, deep learning has exhibited remarkable efficacy as a formidable instrument to traditional approaches by providing better environmental perception, decision-making, and adaptability. This review provides valuable insights into SLAM technology and explores the integration of deep learning techniques into path planning systems. The review first summarizes fundamentals of both SLAM and deep learning-based navigation, highlight current methodologies, and analyze their advantages and limitations. Subsequently, the review discusses potential future directions in combining SLAM with deep learning to achieve intelligent and efficient autonomous navigation in real-world scenarios.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chenleqian Man
PY  - 2025
DA  - 2025/08/31
TI  - Path Planning Based on SLAM Technology and Deep Learning
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 423
EP  - 433
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_42
DO  - 10.2991/978-94-6463-823-3_42
ID  - Man2025
ER  -