Research on Autonomous Driving Technology Based on Simultaneous Localization and Mapping

DOI

10.2991/978-94-6463-823-3_34How to use a DOI?

Keywords

SLAM; Autonomous Driving; Deep Learning

Abstract

In recent years, research on autonomous driving has gradually gained attention and is regarded as a transformative technology that is expected to reshape people’s travel methods and drive significant advancements in the automotive and transportation fields. SLAM technology is a core component supporting autonomous driving systems, providing vehicles with high-precision environmental perception and autonomous positioning capabilities. This paper analyzes and summarizes eight cutting-edge scientific publications, discussing various SLAM implementation methods, such as LiDAR - based SLAM and Visual SLAM, as well as advanced techniques like Multi-sensor Fusion SLAM and SLAM strategies incorporating deep learning technologies. Additionally, it explores their applications and challenges in autonomous driving. Research indicates that LiDAR SLAM excels in high-precision map construction, but its high cost and sensitivity to environmental factors, such as weather conditions and terrain variations, are key limitations hindering its widespread adoption. Visual SLAM, which relies on image data acquired from cameras, is suitable for autonomous driving systems with limited computing resources and has lower implementation costs. However, it struggles with adaptability in changing lighting conditions and dynamic environments. The new SLAM setup scheme discussed in this paper improves system stability and real-time performance by integrating GNSS, IMU, and deep learning technologies, which are crucial for enhancing navigation reliability and adaptability in autonomous driving. Future research should focus on real-time optimization, computational efficiency improvement, and adaptability to complex driving environments to further advance autonomous driving technology.

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  - Bole Wang
PY  - 2025
DA  - 2025/08/31
TI  - Research on Autonomous Driving Technology Based on Simultaneous Localization and Mapping
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 349
EP  - 356
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_34
DO  - 10.2991/978-94-6463-823-3_34
ID  - Wang2025
ER  -