Application of LiDAR and Visual SLAM Technology in Automatic Driving
- DOI
- 10.2991/978-94-6463-512-6_19How to use a DOI?
- Keywords
- SLAM; Automatic driving; LiDAR; Vision; Point cloud
- Abstract
In recent years, autonomous driving has attracted a great deal of attention in many fields, such as academia and industry. If autonomous vehicles want to operate safely and effectively in complex and dynamic real environments, they must solve their own precise positioning problems. SLAM technology makes a big difference in the technology of automatic driving and automatic parking. This paper discusses the principle and application of slam technology based on laser and vision in automatic driving. This paper also sets a specific scene in an underground parking lot, evaluates the positioning methods proposed by LiDAR and vision camera to realize automatic driving, and compares them with relevant existing research methods. It is found that SLAM technology based on LiDAR and vision can greatly optimize the function of automatic driving. Finally, this paper presents the existing problems and future development direction of SLAM technology in the field of automatic driving.
- Copyright
- © 2024 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.
Cite this article
TY - CONF AU - Tailong Shi PY - 2024 DA - 2024/09/23 TI - Application of LiDAR and Visual SLAM Technology in Automatic Driving BT - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024) PB - Atlantis Press SP - 160 EP - 171 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-512-6_19 DO - 10.2991/978-94-6463-512-6_19 ID - Shi2024 ER -