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This paper introduces an approach to classify robot environments based on planar segments extracted from 3D data. In a preprocessing step, point data from a 3D range sensor is transformed to planar patches, i.e. raw data is transformed to a mid level geometric representation. This step allows for a robust, simple and straightforward feature extraction. The features are fed into a learning algorithm,...
This paper presents a feature based 3D mapping approach with regard to obtaining compact models of semi-structured environments such as partially destroyed buildings where mobile robots are to carry out rescue activities. To gather the 3D data, we use a laser scanner, employing a nodding data acquisition system mounted on both real and simulated robots. Our segmentation algorithm comes up from the...
In this paper, we investigate the problem of 3D object categorization of objects typically present in kitchen environments, from data acquired using a composite sensor. Our framework combines different sensing modalities and defines descriptive features in various spaces for the purpose of learning good object models. By fusing the 3D information acquired from a composite sensor that includes a color...
To solve the range scan matching problem, this paper describes a matching rule for points in polar coordinate, i.e. PPMR (Polar Point Matching Rule), which takes into account both rotation and translation error of the scanner displacement at the same time, and can be applied for both 2D and 3D scans. Based on PPMR, a fast correspondence search algorithm is developed taking advantage of the structure...
There are many boundaries of artificial objects which are appeared as edges in the image. These line segments as edge impose information for understanding indoor environment. We obtain 3-dimension positional information as we use a stereo camera. There are three dominant vanishing points(VPs) in 3D world. We can separate lines and estimate forward direction by using VP. Groups of such coinciding lines...
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