학술논문

A Visual-Inertial SLAM-Based Localization Method for Intelligent Vehicles in Weak Textured Environments
Document Type
Conference
Source
2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE) Advanced Algorithms and Control Engineering (ICAACE), 2024 7th International Conference on. :1538-1544 Mar, 2024
Subject
Components, Circuits, Devices and Systems
Robotics and Control Systems
Signal Processing and Analysis
Location awareness
Visualization
Simultaneous localization and mapping
Intelligent vehicles
Feature detection
Merging
Feature extraction
weak textured environments
intelligent vehicle localization
SLAM
features fusion
Language
Abstract
The accurate extraction of feature points has a significant impact on the pose estimation of visual SLAM system, in weak textured environments such as underground parking lots, robust feature point extraction poses significant challenges. In order to solve the problem, this paper proposes a weak texture environment feature extraction method that combines point and line features to improve the localization performance of visual inertial systems (PIL-VIO). Firstly, to obtain stable visual input features, the traditional line feature detection algorithm is improved by parameter tuning, line segment merging, and length-based filtering strategies, which enhances the efficiency and robustness of line feature detection. Secondly, in order to realize the tight coupling of visual and inertial information, this paper constructs a joint optimization function by combining the line features reprojection error with the point features reprojection error, IMU measurement errors, and marginalized prior constraints in a nonlinear optimization framework based on sliding window and marginalization strategies. Finally, the experimental results on the EUROC dataset show that, compared to traditional visual localization algorithms, PIL-VIO has significant advantages in terms of system positioning accuracy and robustness.