• Journal of Information Processing Systems
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• v.16 no.2
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• pp.406-420
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• 2020

In order to make up the deficiencies of the existing research results which cannot effectively deal with the nearest neighbor query based on the line segments in obstacle space, the k nearest neighbor query method of line segment in obstacle space is proposed and the STA_OLkNN algorithm under the circumstance of static obstacle data set is put forward. The query process is divided into two stages, including the filtering process and refining process. In the filtration process, according to the properties of the line segment Voronoi diagram, the corresponding pruning rules are proposed and the filtering algorithm is presented. In the refining process, according to the relationship of the position between the line segments, the corresponding distance expression method is put forward and the final result is obtained by comparing the distance. Theoretical research and experimental results show that the proposed algorithm can effectively deal with the problem of k nearest neighbor query of the line segment in the obstacle environment.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.129-135
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• 2016

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which "global" refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.568-576
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• 1999

Using a laser sheet beam projector combined with a CCD-Camera, an efficient technique to recognize complex surface of curvature and lane has been demonstrated for the purpose of mobile robot navigation. In general, obstacles of indoor environments in the field of SLIT-RAY plane are captured as segments of an elliptical arc and a line in the camera image. The robot has been capable of moving along around the obstacle in front of it, by recognizing the original shape of each segment with the differential coefficient by means of least squares method. In this technique, the imaged pixels of each segment, particularly elliptical arc, have been converted into a corresponding circular arc in the real-world coordinates so as to make more feasible the image processing for the position and radius measurement than conventional way based on direct elliptical are analyses. Advantages over direct elliptical cases include 1) higher measurement accuracy and shorter processing time because the circular arc process can reduce the shape-specifying parameters, 2) no complicated factor such as the tilt of elliptical arc axis in the image plane, which produces the capability to find column position and radiua regardless of the camera location . These are essentially required for a mobile robot application. This technique yields an accuracy less than 2cm for a 28.5cm radius column located in the range of 70-250cm distance from the robot. The accuracy obtained in this study is sufficient enough to navigate a cleaning robot which operates in indoor environments.