汽車導(dǎo)航系統(tǒng)畢業(yè)論文中英文資料外文翻譯--使用gis數(shù)據(jù)庫和激光掃描技術(shù)為汽車導(dǎo)航系統(tǒng)獲取路標(biāo)(編輯修改稿)
2025-06-25 22:41 本頁面
【文章內(nèi)容簡介】 and data structures. Thus, the main problem lies in identifying suitable landmarks and evaluating their usefulness for navigation instructions. In this paper, we show how existing databases can be exploited to tackle the first problem, while laser scanning data can be used to approach the second. 2VISIBILITY ANALYSIS USING LASER SCANNING DATASETS Visibility Analysis we can do better if we base the visibility analysis directly on the DSM from laser scanning. We will not obtain “beautiful” visualizations but instead a rather good estimate on which buildings can be seen from any viewpoint (Fig. 4(c)). We realized this approach as follows. For any viewpoint, the position and viewing direction define the exterior orientation of a virtual camera of given horizontal and vertical viewing angle. This virtual camera represents the driver’s view. The height is derived from the DSM itself, whereas the viewing angle can be obtained from the orientation of the corresponding street segment in the GDF dataset. The virtual image plane is then rastered, each pixel defining a ray in object space. All the rays are traced in object space to determine intersections with the DSM. For each hit, the corresponding object number is obtained by a lookup in an image containing rastered ground plan id’s. Although this method is similar to “ray tracing” used in puter graphics and often assumed to be putationally expensive, it is actually quite fast since (a) we are interested only in the first hit of the ray, and (b) the DSM is only, so each column in the virtual image plane can be puted efficiently from bottom to top, marching in increasing distance in object space. Tracking Visibility In the last section, visibility was puted for a single view. However, landmarks selected for a routing instruction must be visible during the entire manoeuvre. This can be checked by tracking the visibility of objects along the trajectory defined by the corresponding manoeuvre. For our first experiment, we use only a crude approximation for the visibility, namely the area covered by the projection of the corresponding object on the virtual image plane. Figure 1 shows an example. We assume that the white polygon is the trajectory we want the driver to use. The question then is if the town hall, identified to be a landmark by the methods of section 5, is a suitable object which can be used in a 中英文資料 landmarkbased instruction such as ’pass to the right of the town hall’. To this end, our algorithm traces the entire trajectory, generating virtual views at equidistantly spaced positions and in the orientation defined by the trajectory. For each such view, the area covered by each object on the virtual image plane is determined. Figure 2 shows a plot of all those areas along the trajectory of figure 1. One can see the typical ’peaked’ curves generated as objects appear, grow larger and finally disappear as the viewing position passes by. In this special case, one sees also that many objects bee visible around frame number 65, which is when the view widens as the position leaves the narrow street and enters the plaza in front of the town hall. In order to answer if the town hall is a suitable object, a look on figure 2 reveals that the corresponding curve (shown in bold red) is largest for frame numbers 65 to 115 (with a small exception around frame 100), . the town hall is the largest object in the driver’s view. Moreover, the curve is larger than
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