2889.F汽车导航系统中的NAVI画面迁移部分的设计与实现 外文文献.doc

《2889.F汽车导航系统中的NAVI画面迁移部分的设计与实现 外文文献.doc》由会员分享，可在线阅读，更多相关《2889.F汽车导航系统中的NAVI画面迁移部分的设计与实现 外文文献.doc（6页珍藏版）》请在三一办公上搜索。

1、毕业设计（英语文献）英文题目：DESIGN AND IMPLEMENTATION OF THENAVI MENU TRANFER IN THE CAR NAVIGATION SYSTEM 中文题目：汽车导航系统中Navi画面迁移设计与实现学 院 电子与信息工程学院 专 业 计算机科学与技术 姓 名 学 号 指导教师 2008年 6 月EXTRACTING LANDMARKS FOR CAR NAVIGATION SYSTEMS USING EXISTING GIS DATABASES AND LASER SCANNINGKEYWORDS: Car Navigation, Landmarks,

2、GIS, Laser Scanning.ABSTRACTTodays 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 navigation, which is the most natural navigation concept for humans and which also plays an importa

3、nt 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 be quite pretentious considering the challenge to deliver such information for databases co

4、vering 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 building heights and visibility, we show how this can be derived from laser scanning data.1 INTR

5、ODUCTIONModern 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 guidance in form of digital maps, driving direction pictograms, and spoken language driving ins

6、tructions (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 reliable route guidance.However, the original concept of delivering the instructions has not chan

7、ged 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 the street network typically are not visible from a greater distance due to the low driver

8、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. Obviously, the introduction of buildings as landmarks together with corresponding spoken in

9、structions (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 major modification of systems and data structures. Thus, the main problem lies in identifying

10、 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.2 NAVIGATION USING LANDMARKSThere are two different kinds of route

11、 directions to convey thenavigational information to the user: either in terms of a description(verbal instructions) or by means of a depiction (route map).According to (Tversky and Lee, 1999) the structure and semantic content of both is equal, they consist of landmarks, orientation and actions. Us

12、ing landmarks is important, because they serve multiple purposes in wayfinding: they help to organize space, because they are reference points in the environment and they support the navigation by identifying choice points, where a navigational decision has to be made (Golledge, 1999). Accordingly,

13、the term landmark stands for a salient object in the environment that aids the user in navigating and understanding the space (Sorrows and Hirtle, 1999). In general, an indicator of landmarks can be particular visual characteristic, unique purpose or meaning, or central or prominent location. Furthe

14、rmore, landmarks can be divided into three categories: visual,cognitive and structural landmarks. The more of these categories apply for the particular object, the more it qualifies as a landmark (Sorrows and Hirtle, 1999). This concept is used by(Raubal and Winter, 2002) to provide measures to spec

15、ify formally the landmark saliency of buildings: the strength or attractiveness of landmarks is determined by the components visual attraction (e.g. consisting of facde area, shape, color, visibility),semantic attraction (cultural and historical importance, explicit marks, e.g. shop signs) and struc

16、tural attraction (nodes (important intersection), boundaries (parting elements like rail tracks or rivers), regions (building blocks). The combination of the property values leads to a numerical estimation of the landmarks saliency.A study of (Lovelace et al., 1999) includes an exploration of the ki

17、nds and locations of landmarks used in instructions. It can be distinguished between four groups: choice point landmarks (at decision points), potential choice point landmarks (at traversing intersections), on-route landmarks (along a path with no choice)and off-route landmarks (distant but visible

18、from the route). A major outcome of the study is that choice point and on-route landmarks are the most used ones in route directions of unfamiliar environments.The choice of an appropriate landmark depends on the navigation context and application mode: pedestrians or car drivers. Accordingly,there

19、are different studies for both user groups, dealing with the when, why and how landmarks are used in instructions. Because of the different conditions (moving speed, visual field, arbitrary movement or constrained to road network), studies targeted at pedestrians (Michon and Denis, 2001, Lovelace et

20、 al.,1999, Winter, 2002) as well as car drivers (Burnett, 1998, Burnettet al., 2001) have been undertaken. The study of (Burnett,1998) reveals some of the underlying factors for good landmarks which should be considered for designing route guidance systems. Some of the important factors are permanen

21、ce, uniqueness and visibility of the landmark.3 CAR NAVIGATION SYSTEMS3.1 Components of Car Navigation SystemsFigure 1 shows the components of a modern car navigation system.Typically, the cars position is determined by combining signals from a GPS receiver, an angular rate sensor, as well as an odo

22、meter (speed) signal from the wheels. Since the absolute position given by GPS might be quite wrong, especially in densely buildup areas, it is corrected so as to fit to the digital map which is nowadays usually obtained from an onboard mass storage suchas a CD or DVD. This process is called map mat

23、ching and is realized as a multipath matching which always tracks (and rates) several possible positions in the map simultaneously. Altogether, this positioning is most of the time sufficiently accurate and reliable, working even during longer GPS outages.One important module in Fig. 1 is the route

24、guidance module. It is given an ordered list of edges to be driven from the routing (route planning) module as well as the current position from the positioning / map matching module. From that, it decides when to issue which instructions to the driver. For natural language instructions, this can be

25、 divided into “early warnings” (such as keep right or prepare to turn right) and “immediate instructions”(such as turn right now). When thinking about landmark basedcar navigation, one has to keep in mind that this functionality has to be provided as well. Particularly, landmarks are only useful for

26、 driving instructions such as turns if they are visible at a sufficient distance from the decision point, and they do not disappear as the driver is approaching this point.3.2 Digital MapsThe maps used by car navigation systems not only contain the geometry and connectivity of the road network but a

27、lso 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 Files 3.0, 1995). Of particular interest are points of interest (POI) which include museums, theaters, cultural centers, cit

28、y 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, it is converted to the proprietary formats finally found on the map CD or DVD. This conversion is highly nontrivial since t

29、he data has to be transformed from a descriptive form into a specialized form supporting efficient queries by the car navigation system. Often, structures and values are precomputed by this conversion process in order to relieve the navigation systems online resources such as bandwidth and CPU time.

30、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 navigation systems, the angles between all streets joining at an intersection are stored. It is during this conversion process

31、 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 from a cadastral map and laser scan data to identify suitable landmarks. An important point is that the additional datasets a

32、re 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 withthe per-intersection information already stored in proprietary map formats. Thus, the technical integration of landmark-based inst

33、ructions 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 LASER SCANNING AND CITY MODELSDuring the 1990s, airborne laser scanning became available as a new method for obtaining surface

34、 models. Subsequently, the scanning systems were improved and direct georeferencing became feasible with sufficient accuracy. Today, airborne laser scanning is a mature technology with a multitude of companies offering systems and services (Baltsavias, 1999). Scanning of very large areas is possible

35、, 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 laser scanners produce dense point clouds of the earths surface directly (Baltsavias et al., 1999). They are particularly suitable

36、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 the re-flectance, as well as first, last or multiple return pulses, which allows to separate tree canopy and ground (Kraus and

37、 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 images. Especially, the automatic generation of city models has been and still is an intense research field, the discussion of wh

38、ich 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,Baltsavias et al., 2001).However, there is still substantial research effort necessary until highly automated object extraction sys

39、tems working reliably become available. On the other hand, three-dimensional object information is still far from being common in todays existingGIS databases. In consequence, in this paper we consider using two-dimensional GIS databases in combination with laser scanner DSMs on an iconic level, wit

40、hout explicitly reconstructing the three-dimensional shape of the objects as separate entities. Figure 2 shows an example of the data sources used, which is a DSM from laser 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.Steeets from GDF dataset (white) and building groudplans from cadastral map (black). Image shows part of Stuttart, Germany.