Improvement of SLAM Localization Accuracy in AR by Enhancing YOLOv8

Authors

Jia Liu, Tianchang Research Institute, Nanjing University of Information Science and Technology, Chuzhou 239356, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing 210044, China
Zengwei Zhang, Tianchang Research Institute, Nanjing University of Information Science and Technology, Chuzhou 239356, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Dapeng Chen, Tianchang Research Institute, Nanjing University of Information Science and Technology, Chuzhou 239356, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing 210044, China
Nanxuan Huang, Tianchang Research Institute, Nanjing University of Information Science and Technology, Chuzhou 239356, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Bin Wang, Tianchang Research Institute, Nanjing University of Information Science and Technology, Chuzhou 239356, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Hong Song, Tianchang Research Institute, Nanjing University of Information Science and Technology, Chuzhou 239356, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract

Abstract: In the presence of dynamic interference in the environment, traditional simultaneous localization and mapping (SLAM) methods often experience reduced precision and stability in the registration of virtual objects during three-dimensional registration in augmented reality (AR). To address these issues, an improved method for dynamic scenes based on semantic segmentation and optical flow tracking was proposed. The convolutional block attention module (CBAM) attention mechanism was incorporated into YOLOv8 to enhance its focus on dynamic objects in the environment, thereby improving detection performance and accuracy. The semantic segmentation functionality of the improved YOLOv8 was integrated into the front-end of ORB-SLAM3 to segment dynamic objects in the scene and remove dynamic feature points that affect map construction. The optical flow method was further used to track moving objects, thereby improving the positioning accuracy of the camera. Validation was conducted on the TUM dataset and in real-world scenarios. The results indicate that, compared to traditional ORBSLAM3, the proposed method improves positioning accuracy in dynamic scenes, significantly enhancing the stability of 3D registration in AR.

Recommended Citation

Liu, Jia; Zhang, Zengwei; Chen, Dapeng; Huang, Nanxuan; Wang, Bin; and Song, Hong (2025) "Improvement of SLAM Localization Accuracy in AR by Enhancing YOLOv8," Journal of System Simulation: Vol. 37: Iss. 11, Article 1.
DOI: 10.16182/j.issn1004731x.joss.24-0564
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss11/1

First Page

2701

Last Page

2713

CLC

TP391.9

Recommended Citation

Liu Jia, Zhang Zengwei, Chen Dapeng, et al. Improvement of SLAM Localization Accuracy in AR by Enhancing YOLOv8[J]. Journal of System Simulation, 2025, 37(11): 2701-2713.

Corresponding Author

Chen Dapeng

DOI

10.16182/j.issn1004731x.joss.24-0564