Visual Simultaneous Localization and Mapping (SLAM) systems frequently encounter challenges in low-texture environments, resulting in suboptimal performance. Integrating line features into SLAM has emerged as an effective solution. However, a significant drawback of line feature-based SLAM is its limited capability for real-time data processing. Hence, we present EGLT-SLAM, a real-time point-line fusion visual-inertial SLAM system. To promote the system’s robustness in complex environments, the traditional line extraction algorithm is enhanced to assist the feature tracking. An optical flow tracking model specifically designed for line features is developed to further optimize the real-time performance of the SLAM system. Our approach involves extracting line segments from individual frames and tracking them across multiple frames. Decisions for line segment extraction are based on the image entropy change threshold of specified keyframes within the sliding window. Furthermore, a line reprojection error metric that is independent of the endpoints of the line segments is introduced. Experimental results demonstrate that accurate and real-time localization is achieved by EGLT-SLAM even in challenging scenarios with weak textures, illumination changes, and motion blur.

Global navigation satellite systems (GNSSs) exhibit significant performance limitations in signal-deprived environments such as indoor spaces and underground spaces. Although visual SLAM has emerged as a viable solution for ego-motion estimation in GNSS-denied areas, conventional approaches remain constrained by static environment assumptions, resulting in a substantial degradation in accuracy when handling dynamic scenarios. The EMS-SLAM framework combines the geometric constraints and semantics of SLAM to provide a real-time solution for addressing the challenges of robustness and accuracy in dynamic environments. To improve the accuracy of the initial pose, EMS-SLAM employs a feature-matching algorithm based on a graph-cut RANSAC. In addition, a degeneracy-resistant geometric constraint method is proposed, which effectively addresses the degeneracy issues of purely epipolar approaches. Finally, EMS-SLAM combines semantic information with geometric constraints to maintain high accuracy while quickly eliminating dynamic feature points. Experiments were conducted on the public datasets and our collected datasets. The results demonstrate that our method outperformed the current algorithms of SLAM in highly dynamic environments.

Localization accuracy in dynamic Simultaneous Localization and Mapping (SLAM) is degraded when moving objects violate the static-environment assumption. This paper proposes a dynamic Visual SLAM (VSLAM) method that integrates pseudo-semantic segmentation with weighted optimization to address the issue. First, an adaptive feature extraction module is designed, which establishes a Gaussian distribution model for intra-frame features and combines it with an inter-frame matching quality function to overcome the limitations of traditional feature extraction methods. Second, a depth-based pseudo-semantic segmentation method is proposed, where the K-means clustering algorithm is employed to perform clustering analysis on depth information, generating accurate dynamic object masks for precise exclusion of dynamic features. Finally, a weighted optimization model based on epipolar constraints is constructed. By calculating the distance from feature points to epipolar lines and assigning weights accordingly, the model suppresses interference from dynamic feature points while enhancing the contribution of high-confidence static feature points. Experimental results demonstrate that, compared to ORB-SLAM3, the proposed method reduces the root mean square error (RMSE) of the Absolute Trajectory Error (ATE) by approximately 98.5% on the TUM dataset, while outperforming other existing state-of-the-art methods in terms of localization accuracy and robustness.

Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in autonomous navigation, especially in Internet of Things (IoT) application scenarios. However, dynamic objects and rapid camera motion often introduce image blur and feature matching errors, thereby reducing the accuracy of pose estimation. To address these problems, we propose BD-SLAM, a robust and real-time RGB-D visual SLAM framework. Different from existing dynamic SLAM frameworks, we construct an adaptive image deblurring filter to restore image clarity, thereby providing reliable visual input. In addition, we propose a geometric constraint method based on 3D motion field estimation, which overcomes the limitations of relying solely on semantic priors and improves the accuracy of dynamic feature rejection. Furthermore, we design a semantic-guided dynamic point suppression algorithm, which preserves reliable static features while significantly reducing the computational overhead of geometric constraint. The proposed system is evaluated on the TUM, Bonn, and self-collected RGB-D datasets. Experimental results demonstrate that BD-SLAM achieves higher localization accuracy and robustness in dynamic environments compared with state-of-the-art SLAM methods.