Accuracy Of Point Matching Research Articles

BY-SLAM: Dynamic Visual SLAM System Based on BEBLID and Semantic Information Extraction.

SLAM is a critical technology for enabling autonomous navigation and positioning in unmanned vehicles. Traditional visual simultaneous localization and mapping algorithms are built upon the assumption of a static scene, overlooking the impact of dynamic targets within real-world environments. Interference from dynamic targets can significantly degrade the system's localization accuracy or even lead to tracking failure. To address these issues, we propose a dynamic visual SLAM system named BY-SLAM, which is based on BEBLID and semantic information extraction. Initially, the BEBLID descriptor is introduced to describe Oriented FAST feature points, enhancing both feature point matching accuracy and speed. Subsequently, FasterNet replaces the backbone network of YOLOv8s to expedite semantic information extraction. By using the results of DBSCAN clustering object detection, a more refined semantic mask is obtained. Finally, by leveraging the semantic mask and epipolar constraints, dynamic feature points are discerned and eliminated, allowing for the utilization of only static feature points for pose estimation and the construction of a dense 3D map that excludes dynamic targets. Experimental evaluations are conducted on both the TUM RGB-D dataset and real-world scenarios and demonstrate the effectiveness of the proposed algorithm at filtering out dynamic targets within the scenes. On average, the localization accuracy for the TUM RGB-D dataset improves by 95.53% compared to ORB-SLAM3. Comparative analyses against classical dynamic SLAM systems further corroborate the improvement in localization accuracy, map readability, and robustness achieved by BY-SLAM.

Adaptive parameter local consistency automatic outlier removal algorithm for area-based matching

Abstract. Due to the influence of image differences and matching methods, geometric calibration of remote sensing images often results in the extraction of control points with inevitable outliers. Moreover, it is susceptible to limitations imposed by locally constrained outlier rejection methods, making it challenging to automatically remove relatively small gross errors. This paper introduces an adaptive parameter local consistency automatic outlier removal algorithm, referred to as APLC. Initially, we construct k-nearest neighbors for each pair of matching points, deriving distance and topological uncertainty based on the accuracy of point matching. Subsequently, we conduct cross-validation on the uncertainty between the two pairs of vectors formed by points within the neighborhood, aiming for parameter adaptation. Finally, a cost-defined function is introduced to assess the consistency of local structures. Through a two-stage outlier removal strategy, matching points that do not maintain local structural consistency are eliminated. To assess the effectiveness of the proposed algorithm, we conduct experimental comparisons using region-based initial matching results from the FY-3D remote sensing dataset, demonstrating its superiority compared to three state-of-the-art methods.

Enhanced Motion Consistency and Guided Diffusion Feature Matching for 3D Reconstruction

Feature matching is one of the key steps to restore a 3D model from an image. To effectively improve the quality of 3D reconstruction, an enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction is presented. Firstly, based on the grid-based motion statistics algorithm, an enhanced motion consistency concept is proposed by adding a threshold <italic>β</italic>, which enhances the judgment condition of true and false matching points, avoids the false matching of highly similar features, and improves the initial matching points accuracy. Then, the RANSAC algorithm is used for feature point matching optimization to filter out outliers and further improve the feature point matching accuracy. Finally, a guided diffusion concept that combines guided matching and motion consistency is proposed, which reduces the possibility of concentrated distribution in the part of the image, thereby improving the feature points matching number and the 3D model stability. Experiments on 618 pairs of images in the public 3D reconstruction datasets demonstrate that this algorithm can achieve better performance in feature matching and 3D reconstruction. For the success percentage of pose estimation less than 1° error threshold, the proposed algorithm is 22.58% and 12.90% higher than the SIFT-based ratio test algorithm and the GMS algorithm, respectively. In particular, it is 46.15% and 30.77% higher on repeated texture image pairs.

Fast Decomposition Algorithm Based on Two-Dimensional Wavelet Transform for Image Processing of Graphic Design

Graphic design is an important part of the design field today. In this era of information explosion, designs that can deliver information faster and more accurately are bound to gain popularity among the public. In this paper, we propose a fast decomposition algorithm image processing method based on a new transform of the wavelet transform, which mainly addresses the problems of large computation of feature points and long-time consumption of traditional image processing algorithms. Firstly, the second-order decomposition of the image is performed by wavelet function to obtain the low-frequency components of the image, and the wavelet gradient vector is used to extract feature points from the overlapping regions of the low-frequency image so that the transformation parameters of feature points can be obtained quickly under the low-frequency image to guide the feature point extraction under the high-frequency image; on this basis, an improved algorithm of image processing based on the fast decomposition algorithm of two-dimensional wavelet transform with planar design is proposed. Using the properties of one-way matching and directional consistency of feature point constraints, the mismatched point pairs are effectively eliminated to improve the feature point matching accuracy and real-time performance. Finally, the effectiveness and feasibility of the proposed method are verified by two sets of experiments.