汽车导航系统毕业论文中英文资料外文翻译文献

1、汽车导航系统中英文资料外文翻译文献 使用gis数据库和激光扫描技术为汽车导航系统获取路标索引现在的汽车导航系统以地图，图形，以及声音的形式提供给用户行驶中的信息，然而他们还远远不能支持基于道路标记的导航，而这也是对我们来说更简单的导航理念，并且这也在不久要实现的个人导航系统中占据重要的位置。为了提供这样的一种导航，第一步就要识别恰当的道路标记乍一看似乎很简单，但是如果考虑到要把覆盖了欧洲、北美、日本大部分地区的信息传输给数据库的挑战，我们就有理由自命不凡了。在这里，我们将讲解从已存在的gis数据库中获取道路标记的方法。因为这些数据库大多数没有包含建筑物的高度和视图信息，我们将展示这些信息怎样从

2、激光扫描数据中分离出来。1简介1995年在上层阶级的汽车里汽车导航系统就已经出现了，而且现在几乎可以在任何样式的汽车中找到导航系统。他们是相对复杂和成熟的系统可以以数字地图，行驶方向图形，以及行驶中的声音信息提供路线导航。回溯1980年汽车导航系统开始兴起的时候，一些大的问题都得到了解决：例如绝对位置，适合导航的大量地图的提供，快速算路以及可靠的路线导航。然而，传送这些信息的原始概念并没有得到较大的改善。声音的导航仍然用相对小的提示：（例如 现在向右转），这只涉及到了道路分布的属性。这不是最理想的，因为1）路线分布的特征在较大距离的时候是不可见的，这是因为司机受局限的位置以及视角，2）人们最习

3、惯的导航方式是通过道路标记，也就是沿路中一系列的可识别可记忆的的图像的提供。很明显，作为道路标记的建筑物的提示与声音提示结合起来，将是导航发展中更人性化的一个方向，就像我们下边讨论的那样，这将很好的集成到今天的汽车导航系统中去因为不意味着对系统和数据结构的大的改动。所以，主要的问题在于识别合适的道路标记以及估计他们对于导航提示的可用性。这里，我们将解释已存的数据库怎样开发以解决第一个问题，而激光浏览数据库将解决后一个。2使用激光扫描数据集的可见性分析2.1可见性分析如果我们以来自激光扫描的dsm上直观的可见性作为分析的基础，我们会做的更好。我们将不会获得像当初估计的那样使建筑物从任何的地点都被

4、清晰地看见。 我们依照下列各项的方式，对于任何的观察点的位置和观看方向定义给予的水平线和垂直的一个虚拟的照相机的外部方位看角。 这个虚拟的照相机表示为驾驶者的视野。高度起源于dsm本身,然而看角从 gdf 数据组中对应的街道的固定方位被获得。虚拟图像的平面然后被光栅过滤, 每个图素定义物体空间的一道光线。所有的光线在物体空间中被追踪并且用 dsm 决定交集。对于每次击中，对应的物体数据被试映图的虚拟图像查询获得。虽然这个方法与 光线追踪 类似并用在计算机图形方面和平时假设计算中，但是自从我们只在光线的第一个击中方面感兴趣之后，它实际上相当速，而且dsm只是2.5 d,虚拟图像的飞机以此下去可能

5、从底部到顶端被有效率地计算,向逐渐增加的物体空间进军。2.2 追踪可见性在最后的一个区段中，单一视野被计算。 然而,道路标记被一个路线排定指令选择，而且一定在整个调遣期间是看得见的。这可能沿着对应的调遣定义的轨道追踪物体的可见性。对于我们的第一次实验，我们使用只有一个自然的附近区域作为可见, 即被虚拟图像上的飞机的对应物体覆盖的区域。 图1显示了一个例子。 我们假设白色的多角形是我们驾驶者使用的轨道。然后问题是如果以其他的方法识别它是一个道路标记的城镇大厅,是一个可以被道路标记唯一表示的适当物体。对这一次的结论，我们的运算法则是追踪整个的轨道,以等距离隔开的位置和在轨道旁边的固定方位产生罚款者

6、眼中虚拟的视野。对于每个如此的视野，虚拟图像中的飞机上的每个物体覆盖的区域被决定。图 2显示沿着图1的轨道所有的那些区域的一种情况。当物体出现的时候, 能产生典型的有遮掩的曲线, 变化比较大和最后消失的就如被途径人所观察。 在这种特别的情形中，当位置是城镇门厅之前并留下狭窄的街道和进入广场的时候，或视野弄宽的时候，许多物体在附近看变成规格为65号。为了确定城镇大厅是否为一个适当的物体, 从图2上的对应的曲线看，从规格65到115号是最大的,也就是城镇大厅是驾驶者视野中最大的物体。而且，曲线比从规格13号开始的更大,这意味城镇大厅是一少部分大约在进入广场的之前100 公尺处被看见( 可能是决定点

7、) 因此，在这种情况下我们既能查出显示比较大的物体，也能在驾驶者的视野中将他最早显示出来。图1 俯视图上的轨迹线图2：基于框架数字的可见性划分3数据地图汽车导航系统使用的地图不仅包含几何学和道路网络的连接性而且包含了大量的关于物体，属性和关系的附加信息。一个好的观点能够从欧洲的标准获得，举例来说，（年月的地理数据文件），其中包括了博物馆，戏院，文化中心和市政厅等的信息。地图数据是被诸如电子地图的地图数据库厂商获得并通过交换的方式提供给汽车导航系统生产商的（例如）。在那里，它被转换到最后在地图激光唱碟或数字化视频光上被发现的专有格式。数据必须从一种描述形式转换成被汽车导航系统支援的另一种被特殊化

8、的形式，这转变是高度非凡的。时常，结构和价值被这个转换过程预先计算了，目的是为了要减轻航行系统的在线资源 , 例如带宽和时间。这个模块的其中一部分也是为每个十字路口产生一个点阵式，目的是描述所有的可能转向的组合。在汽车导航系统中使用了众所周知的箭头符号来标识，这就需要所有道路的十字路口的交汇情况将被存储。在转向过程中，对于带有路标的汽车导航系统的附加信息会被完整化。在本文中，概括说明了是怎样通过与地图数据和激光扫描数据结合来确定道路几何图形的适合的路标，重要的一点是那些附加的数据信息仅仅在这个转换过程中被使用。在那之后，仅仅是基于路标的行使指示还存在，这些是行使指示可能在一种非常紧凑的形式下被

9、编码，并且要与每一个十字路口各自的已被存储在专有地图格式的数据信息相协调。因此，路标技术的整合没有在现在的汽车导航系统中造成障碍，这些主要问题是来自那些用自动或半自动方法的指令中的。4 激光扫描和城市模型在二十世纪九十年代，靠空气传播的激光扫描作为获得表面的模型的新方法变得可用。随后，扫描系统提高了并且指引全球范围也因为足够的精度变得可行。今天，靠空气传播的激光扫描是一项成熟的技术为大多数公司提供系统和服务。扫描很大的区域是可能的，例如整个荷兰已经被扫描过了，德国的baden-wurttemberg州也正在进行扫描，他们中每一个的面积都超过了30平方千米。天线激光扫描机直接地生产地球的表面密集

10、的点云 (baltsavias et al。,1999). 他们对获得密集的都市区域的数传表面模型 (dsms) 是特别地适当的, 如同他们保存跳跃边缘一样相当好。 大多数的系统能够测量不只有高度, 也有反射系数, 和首先,最后的或多样的回行脉冲,他们允许分开树形天篷和地面。 (kraus 和 rieger,1999)主要的问题是怎样从激光扫描数据组中获取关于人造结构的符号信息，可能和天空的或陆地的图像联合。尤其, 自动机械世代的城市模型是而且仍然是一个强烈的研究领域, 这个讨论是超过本文的范围的。 在这一问题上，读者可以咨询“ascona 工作室”的优秀的成果。 (grun et al.,

11、1995, grun et al., 1997,baltsavias et al., 2001).然而，实质性研究努力还是很必要的直到高度自动化的物体获取系统可以可靠地工作。另一方面，三维空间存在的物体信息在今天存在的gis数据库中还远远不是普遍的。所以，在本文中我们将考虑把gis数据库和激光扫描dsms联合起来在一个图标层上，不明确地重建物体的三维空间的形状而当做分开实体。图3展示了一个数据资源被用过的例子，来自正在激光扫描的dsm，使有规则到1米的格子，街道的几何形状用从一个gdf数据组合的中心线表示，而建筑物的轮廓用从地籍图上获得的中心线表示。图3 激光扫描5 结论及前景在本文中，已经概

12、略说明路标是如何被取得的并且评估使用已存在的 gis 和激光扫描数据。 至于路标的取得，我们已经调查基于显示突出建筑物的二种不同的方法。 为了评估导航引导的有用性,我们用了基于来自激光扫描的 dsm 数据的一项可见性分析。 数据挖掘程序必须用真正的数据组来测试。 如果他们在现实世界中使用适当的路标引导，这个结论将会被证实。 除此之外，分析程序必须被扩展到不同的事物类型 (交通建筑,公园,体育运动设备等.) 从 atkis 数据提取举例来说明。不同种类事物的数据预处理方法和当不同数据挖掘运算法则被提供到相同数据时产生的问题必须被调查。萃取的路标的可靠性不得不通过质量测试来决定，目的是为了避免不明

13、确的目标误导用户。更多的依靠路线来决定路标的问题必须被调查: 用户行驶方向和路标质量可见性的影响。当我们只用了 虚拟的图像大小 来估价一个事物的可见性时,有很大的空间来进步。举例来说，如果一个事物被它前面或附近的事物挡住了，或者是整个轮廓的一部分，从虚拟的图像，就能获得远距离的信息。首先激光扫描测量的脉搏能够被整合，目的是为了获得一个比较好的由树导致阻塞的近似值。dsm 也可能被用来提供萃取的附加信息，例如，小塔被它前面的大建筑物挡住这个信息将被确定。跟踪可见性的执行使用等距离的时间取样来代替空间取样，这是基于车辆在临近交叉路口的速度的。最后，存在于gdf数据中的poi数据被使用到可见性分析的

14、扩展是非常有趣的。附件2：外文原文extracting landmarks for car navigation systems using existing gis databases and laser scanningabstracttodays car navigation systems provide driving instructions in the form of maps, pictograms, and spoken language. however, they are so far not able to support landmark-based navigat

15、ion, which is the most natural navigation concept for humans and which also plays an important role for upcoming personal navigation systems. in order to provide such a navigation, the first step is to identify appropriate landmarks a task that seems to be rather easy at first sight but turns out to

16、 be quite pretentious considering the challenge to deliver such information for databases covering huge areas of europe, northern america and japan. in this paper, we show approaches to extract landmarks from existing gis databases. since these databases in general do not contain information on buil

17、ding heights and visibility, we show how this can be derived from laser scanning data.1 introductionmodern car navigation systems have been introduced in 1995 in upper class cars and are now available for practically any model. they are relatively complex and mature systems able to provide route gui

18、dance in form of digital maps, driving direction pictograms,and spoken language driving instructions (zhao, 1997).looking back to the first beginnings in the early 1980s, many nontrivial problems have been solved such as absolute positioning, provision of huge navigable maps, fast routing and reliab

19、le route guidance.however, the original concept of delivering the instructions has not changed very much. still, spoken language instructions use a relatively small set of commands (like turn right now), which only refer to properties of the street network. this is not optimal, since i) features of

20、the street network typically are not visible from a greater distance due to the low driver position and small observing angle, and ii) the most natural form of navigation for humans is the navigation by landmarks, i.e. the provision of a number of recognizable and memorizable views along the route.

21、obviously, the introduction of buildings as landmarks together with corresponding spoken instructions (such as turn right after the tower) would be a step towards a more natural navigation. as we argue below, this would be well integrable into todays car navigation systems as it would not imply a ma

22、jor modification of systems 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 use

23、d to approach the second.2visibility analysis using laser scanning datasets2.1 visibility analysiswe 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 se

24、en 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 drivers view. the height is derived fro

25、m 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

26、. for each hit, the corresponding object number is obtained by a lookup in an image containing rastered ground plan ids. although this method is similar to “ray tracing” used in computer graphics and often assumed to be computationally expensive, it is actually quite fast since (a) we are interested

27、 only in the first hit of the ray, and (b) the dsm is 2.5d only, so each column in the virtual image plane can be computed efficiently from bottom to top, marching in increasing distance in object space.2.2 tracking visibilityin the last section, visibility was computed for a single view. however, l

28、andmarks 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 a

29、rea 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

30、 object which can be used in a landmark-based 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 de-fined by the trajectory. for each such view, the area co

31、vered 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

32、sees also that many objects become 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

33、(shown in bold red) is largest for frame numbers 65 to 115 (with a small exception around frame 100), i.e. the town hall is the largest object in the drivers view. moreover, the curve is larger than zero starting from frame number 13, which means that the town hall is at least partly visible about 1

34、00 meters ahead of the position where the plaza is entered (which could be a decision point). thus, in this case we can verify both that the object appears large and that it appears early enough in the drivers view.figure 1:example trajectory, top view.figure 2: visibility plotted over frame number3

35、 digital mapsthe maps used by car navigation systems not only contain the geometry and connectivity of the road network but also a huge amount of additional information on objects, attributes and relationships. a good overview can be obtained from the european standard gdf, see e.g. (geographic data

36、 files 3.0, 1995). of particular interest are points of interest (poi) which include museums, theaters, cultural centers, city halls, etc.map data is acquired by map database vendors such as tele atlas or navtech and supplied to car navigation manufacturers in an exchange format (such as gdf). there

37、, it is converted to the proprietary formats finally found on the map cd or dvd. this conversion is highly nontrivial since the data has to be transformed from a descriptive form into a specialized form supporting effi-cient queries by the car navigation system. often, structures and values are prec

38、omputed by this conversion process in order to relieve the navigation systems online resources such as bandwidth and cpu time.part of this process is also to generate a matrix for each intersection which describes all possible turn combinations. also, for the well-known arrow pictograms used by car

39、navigation systems, the angles between all streets joining at an intersection are stored. it is during this conversion process where additional information for landmark-based navigation can be integrated. in this paper, we outline how the street geometry given by gdf can be combined with information

40、 from a cadastral map and laser scan data to identify suitable landmarks. an important point is that the additional datasets are used only during the conversion process. after that, only landmark-based driving instructions remain, which can be coded in a very compact form and are compatible with the

41、 per-intersection information already stored in proprietary map formats. thus, the technical integration of landmark-based instructions into current car navigation systems poses no major obstacles, and the main problem is to derive those instructions in some automatic or at least semiautomatic way.4

42、 laser scanning and city modelsduring the 1990s, airborne laser scanning became available as a new method for obtaining surface models. subsequently, the scanning systems were improved and direct georeferencing became feasible with sufficient accuracy. today, airborne laser scanning is a mature tech

43、nology with a multitude of companies offering systems and services (baltsavias, 1999). scanning of very large areas is possible, for example the entire netherlands have been and germanys state of baden-wurttemberg is in the progress of being scanned, each with an area of over 30.000 km2. aerial lase

44、r scanners produce dense point clouds of the earths surface directly (baltsavias et al., 1999). they are particularly suitable for obtaining digital surface models (dsms) in dense urban areas, as they conserve jump edges quite well. most systems are capable of measuring not only the height, but also

45、 the re-flectance, as well as first, last or multiple return pulses, which allows to separate tree canopy and ground (kraus and rieger,1999).the main problem is how to extract symbolic information about man-made structures from laser scanner datasets, possibly combined with aerial or terrestrial ima

46、ges. especially, the automatic generation of city models has been and still is an intense research field, the discussion of which is beyond the scope of this paper. the reader is referred to the excellent proceedings of the “ascona workshops” on this topic (grun et al., 1995, grun et al., 1997,balts

47、avias et al., 2001).however, there is still substantial research effort necessary until highly automated object extraction systems working reliably become available. on the other hand, three-dimensional object information is still far from being common in todays existing gis databases. in consequenc

48、e, in this paper we consider using two-dimensional gis databases in combination with laser scanner dsms on an iconic level, without explicitly reconstructing the three-dimensional shape of the objects as separate entities. figure 3 shows an example of the data sources used, which is a dsm from laser

49、 scanning, regularized to a 1 m grid, the street geometry represented by center lines from a gdf data set, and the outline of buildings from a cadastral map.figure3:laser scan5 conclusion and outlookin this paper, we have outlined how landmarks can be extracted and evaluated using existing gis and l

50、aser scanning data. as for the extraction, we have investigated two different methods based on data mining to reveal prominent buildings. in order to evaluate the usefulness for navigation instructions, we used a visibility analysis based on dsm data from laser scanning. both data mining procedures have still to be tes