Epipolar geometry is a fundamental constraint used in computer vision systems to estimate parameters using correspondence. The most common way to describe epipolar geometry is by means of a 3x3 matrix called the fundamental matrix, and such matrices are used to store the precise geometric information relating a pair of stereo images. Its efficient estimation substantially improves initialization in visual simultaneous localization and mapping (vSLAM), which uses correspondence-based epipolar geometry to determine the trajectory of the camera and a three-dimensional scene. Conventional robust methods for epipolar geometry estimation can become computationally inefficient and inaccurate when there are low-quality correspondences. Because semantic information can be more stable than pixel intensities/descriptors, a novel Faster Region-based Convolutional Network (R-CNN) learning-based approach called the semantic filter is proposed in this paper to address these problems. The semantic filter is first trained on different semantic patches, which are described in terms of their different outlier distributions, providing different semantic labels for image contexts. Then, the patches with low-level semantic labels are filtered out. Finally, precise and robust correspondences can be determined by matches using the high-level semantic contexts, making the correspondence-based calculation more accurate. For dynamic outdoor scenes, the results of extensive experiments show that our semantic filter can help vSLAM localize accurately and robustly on a map from different viewpoints. In a completely static scenario, our semantic filter can remove the low-quality correspondences, enabling the mobile robot to operate well.

Cracks in road surfaces are among the most critical problems in the management of expressways. Their sizes, which not only have severe consequences in relation to expressway safety, decide maintenance timing, which if accurately estimated, can prevent the rapid development of crack disease. Currently, the main techniques used for crack size measurement makes use of either human inspection or sophisticated mobile equipment, which are respectively, very time consuming, labour-intensive, and difficult to implement in civil engineering. Thus, there is great interest in practical and highly efficacious automated crack identification systems coupled with low-cost data-processing sensors. A PTZ (pan/tilt/zoom) camera-based image processing idea is investigated in this paper for crack size measurement in expressways in China. The proposed method consists of four steps: 1) image acquisition, 2) corner-based line size calculation, 3) pixel size calculation, and 4) crack size estimation. A case study of roadway lane images and crack images, collected from China G4/Jingshi, is conducted to demonstrate the feasibility of the proposed method. The results demonstrate that PTZ camera-based image processing is capable of efficiently quantifying cracks, obtaining high accuracy crack size results at range of 0 m to 40 m. The method shows promise for crack size measurement in a cost-effective manner and can provide an objective and timely means for transportation agencies to conduct maintenance plans.

Docking ring is a circular hatch of spacecraft that allows servicing spacecraft to dock in various space missions. The detection of the ring is greatly beneficial to automatic capture, rendezvous and docking. Based on its geometrical shape, we propose a real-time docking ring detection method for on-orbit spacecraft. Firstly, we extract arcs from the edge mask and classify them into four classes according to edge direction and convexity. By developing the arc selection strategy, we select a combination of arcs possibly belonging to the same ellipse, and then estimate its parameters via the least squares fitting technique. Candidate ellipses are validated according to the fitness of the estimation with the actual edge pixels. The experiments show that our method is superior to the state-of-the-art methods, and can be used in real time application. The method can also be extended to other applications.