STO-VINS: A Robust Monocular Visual-Inertial SLAM Based on a Fusion Feature Extraction Algorithm Combining Shi-Tomasi and ORB Wuhan University of Technology

Format:

Book

Conference/Event

Author/Creator:

Li, Jing, author.

Contributor:

Gong, Zeyuan

Liu, Bo

Wu, Jing

Zhang, Guofang

Conference Name:

SAE 2025 Intelligent and Connected Vehicles Symposium (2025-09-19 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

The VINS-Mono algorithm, which is based on a visual-inertial SLAM framework, faces challenges in extracting feature points in regions with weak or repetitive textures and struggles to achieve accurate localization under unstable lighting conditions. This paper proposes STO-VINS, a robust monocular visual-inertial SLAM algorithm that introduces several key innovations in feature extraction. Key innovations of STO-VINS include: (1) an adaptive multi-scale image preprocessing pipeline that combines image scaling, CLAHE enhancement, and Gaussian filtering, reducing computational complexity by 64% while maintaining feature quality; (2) bidirectional Lucas-Kanade optical flow consistency verification with geometric constraint validation, which significantly reduces false tracking rates by 30-40%; (3) a grid-based multi-feature fusion detection strategy combining Shi-Tomasi corner detection and ORB feature extraction, ensuring uniform spatial distribution of features and feature diversity; (4) an intelligent dynamic parameter adjustment system that optimizes detection parameters based on multi-dimensional image quality assessments (brightness histogram analysis, Laplacian blur metric, and Canny edge density); and (5) a smart feature point quality filtering mechanism that implements distance-based deduplication and non-maximum suppression to retain the optimal 100 feature points. These innovations offer three key advantages: enhanced robustness through multi-algorithm fusion and consistency verification, improved computational efficiency through multi-scale processing and grid-based detection, and superior environmental adaptability through intelligent parameter optimization. Experimental validation using the EuRoC dataset shows that STO-VINS achieves a 6.5% improvement in localization accuracy over VINS-Mono in non-loop closure scenarios. Further outdoor scene experiments confirm that, while VINS-Mono suffers from severe trajectory drift, STO-VINS produces trajectories that closely match the experimental route with minimal drift error. The results demonstrate that STO-VINS significantly improves feature point extraction in challenging environments and offers a new paradigm for intelligent, adaptive, and high-efficiency feature tracking in SLAM systems, leading to substantial improvements in real-time performance, system stability, and environmental adaptability

Notes:

Vendor supplied data

Publisher Number:

2025-01-7306

Access Restriction:

Restricted for use by site license