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Volume 15
Issue 19
10.3390/app151910595
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

YSAG-VINS—A Robust Visual-Inertial Navigation System with Adaptive Geometric Constraints and Semantic Information Based on YOLOv8n-ODUIB in Dynamic Environments

by
Kunlin Wang
1,
Dashuai Chai
1,*,
Xiqi Wang
2,
Ruijie Yan
1,
Yipeng Ning
1,
Wengang Sang
1 and
Shengli Wang
3
1
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2
School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
3
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(19), 10595; https://doi.org/10.3390/app151910595
Submission received: 25 August 2025 / Revised: 24 September 2025 / Accepted: 29 September 2025 / Published: 30 September 2025
Versions Notes

Abstract

Dynamic environments pose significant challenges for Visual Simultaneous Localization and Mapping (VSLAM), as moving objects can introduce outlier observations that severely degrade localization and mapping performance. To address this problem, we propose YSAG-VINS, a VSLAM algorithm specifically designed for dynamic scenes. The system integrates an enhanced YOLOv8 object detection network with an adaptive epipolar constraint strategy to effectively identify and suppress the impact of dynamic features. In particular, a lightweight YOLOv8n model augmented with ODConv and UIB modules is employed to balance detection accuracy with real-time efficiency. Based on semantic detection results, images are divided into static background and potentially dynamic regions, and the motion state of these regions is further verified using geometric constraints. Features belonging to truly dynamic objects are then removed to enhance robustness. Comprehensive experiments on multiple public datasets demonstrate that YSAG-VINS achieves superior pose estimation accuracy compared with VINS-Fusion, VDO-SLAM, and Dynamic-VINS. On three dynamic sequences of the KITTI dataset, the proposed method achieves average RMSE improvement rates of 48.62%, 12.18%, and 13.50%, respectively. These results confirm that YSAG-VINS provides robust and high-accuracy localization performance in dynamic environments, making it a promising solution for real-world applications such as autonomous driving, service robotics, and augmented reality.
Keywords: simultaneous localization and mapping; object detection; geometric constraints; real-time; dynamic environments simultaneous localization and mapping; object detection; geometric constraints; real-time; dynamic environments

Share and Cite

MDPI and ACS Style

Wang, K.; Chai, D.; Wang, X.; Yan, R.; Ning, Y.; Sang, W.; Wang, S. YSAG-VINS—A Robust Visual-Inertial Navigation System with Adaptive Geometric Constraints and Semantic Information Based on YOLOv8n-ODUIB in Dynamic Environments. Appl. Sci. 2025, 15, 10595. https://doi.org/10.3390/app151910595

AMA Style

Wang K, Chai D, Wang X, Yan R, Ning Y, Sang W, Wang S. YSAG-VINS—A Robust Visual-Inertial Navigation System with Adaptive Geometric Constraints and Semantic Information Based on YOLOv8n-ODUIB in Dynamic Environments. Applied Sciences. 2025; 15(19):10595. https://doi.org/10.3390/app151910595

Chicago/Turabian Style

Wang, Kunlin, Dashuai Chai, Xiqi Wang, Ruijie Yan, Yipeng Ning, Wengang Sang, and Shengli Wang. 2025. "YSAG-VINS—A Robust Visual-Inertial Navigation System with Adaptive Geometric Constraints and Semantic Information Based on YOLOv8n-ODUIB in Dynamic Environments" Applied Sciences 15, no. 19: 10595. https://doi.org/10.3390/app151910595

APA Style

Wang, K., Chai, D., Wang, X., Yan, R., Ning, Y., Sang, W., & Wang, S. (2025). YSAG-VINS—A Robust Visual-Inertial Navigation System with Adaptive Geometric Constraints and Semantic Information Based on YOLOv8n-ODUIB in Dynamic Environments. Applied Sciences, 15(19), 10595. https://doi.org/10.3390/app151910595

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