The estimation of a fundamental matrix (F-matrix) from two-view images is a crucial problem in epipolar geometry, and a key point in visual simultaneous localization and mapping (VSLAM). Conventional robust methods proposed by the data calculation space, such as Random Sample Consensus (RANSAC), encounter computational inefficiency and low accuracy when the outliers exceed 50%. In this paper, a semantic filter-based on faster region-based convolutional neural network (faster R-CNN) is proposed to solve the outlier problem in RANSAC based F-matrix calculations. The semantic filter is trained using semantic patches tailored by inliers, providing different semantic labels in various image regions. First, the patches classified into the top three bad labels are filtered out during the pre-processing phase. Second, precise and robust correspondences are determined by the remaining high-level semantic contexts. Finally, the inliers are assessed using RANSAC to produce an accurate F-matrix. The proposed algorithm can improve the accuracy of F-matrix calculations, as low-quality feature correspondences are effectively decreased. Experiments on KITTI and ETH sequences illustrate that the 3D position error can be reduced by applying the semantic filter to the ORB-SLAM system. Further, indoor and real environment experiments demonstrate that an effective lower trajectory error is yielded with the proposed approach.
This study proposes a line segment detection that can efficiently and effectively handle non-linear uniform intensity changes. The presented sketching algorithm applies the resistant to affine transformation and monotonic intensity change (RATMIC) descriptor to conduct binary translation in the image pre-processing step, which can remove the unwanted smoothing of the Canny detector in most line detections. The Harris corner detector is applied to catch regions of line segments for the purpose of simulating the composition of sketching and achieving a sense of unity within the picture. Furthermore, the RATMIC descriptor is employed to obtain binary images of the regions of interest (ROIs). Finally, small eigenvalue analysis is implemented to detect straight lines in the ROIs. The experiments conducted on various images with image rotation, scaling, and translation validate the effectiveness of the proposed method. The experimental results also demonstrate that about 30% in the overall coverage of major lines and 20% in the coverage per major line are increased compared with the state-of-the-art line detectors. Moreover, the performance of the proposed method produces a combined advantage of ∼17% in the coverage of line segments over the line segment detector with noisy images.
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