本文为加拿大萨斯喀彻温大学（作者：Anastasia Petrenko）的硕士论文，共66页。

在未知和陌生的环境中寻找路径是一项常见的任务。从纸质地图到基于位置的服务等一系列工具已被引入以协助人类导航。毫无疑问，汽车导航系统可以被认为是最成功的例子，基于位置的服务广泛获得用户的接受。然而，汽车导航的概念并不总是（也许很少）适合行人导航。此外，由于缺乏GPS等绝对定位方法，室内运动物体的精确定位是不可能的。这使得精确的室内跟踪和导航成为一个有趣的问题。室外应用中流行的许多空间分析方法都可以在室内使用。特别是，设计室内导航网络需要a）改善复杂室内的导航体验和b）加强对使用现有定位解决方案收集的室内跟踪数据分析。此类建筑模型应基于图形表示法，并由“节点”和“边”的数量组成，其中“节点”对应于房间的中心位置，“边”表示走廊多边形的中轴，这些多边形在物理上连接这些房间。类似的节点连接应该应用于楼梯和电梯来连接建筑楼层。

为了生成这个模型，我选择了萨斯喀彻温大学的校园作为研究区域，并提出了一种使用ESRI ArcGIS产品创建室内导航网络的方法。该方法首先自动提取校园建筑的几何和拓扑结构，计算各实体之间的距离，设计出它们之间的最短路径。该系统在大学校园内导航，帮助定位教室、办公室或设施。路线的计算基于Dijkstra算法，但可以采用任何网络导航算法。为了显示生成网络的优势，提供了与计算机科学系联合进行的一项研究结果。在一所大学校园内，设计了一个包含37名参与者的实验来收集跟踪数据，以证明室内导航模型的引入如何改进对室内运动数据的分析。根据研究结果可以得出结论，生成的室内网络可以应用于原始定位数据，以提高精度，也可以作为一个独立的工具，用于加强大学校园的路线引导，并扩展由单个或多个建筑物组成的任何容量的室内空间。

Finding ones way in unknown and unfamiliar environments is a common task. A number of tools ranging from paper maps to location-based services have been introduced to assist human navigation. Undoubtedly, car navigation systems can be considered the most successful example of location based services that widely gained user acceptance. However the concept of car navigation is not always (perhaps rarely) suitable for pedestrian navigation. Moreover, precise localization of moving objects indoors is not possible due to the absence of an absolute positioning method such as GPS. These make accurate indoor tracking and navigation an interesting problem to explore. Many of the methods of spatial analysis popular in outdoor applications can be used indoors. In particular, generation of the indoor navigation network can be an effective solution for a) improving the navigation experience inside complex indoor structures and b) enhancing the analysis of the indoor tracking data collected with existing positioning solutions. Such building models should be based on a graph representation and consist of the number of ‘nodes’ and ‘edges’, where ‘nodes’ correspond to the central position of the room and ‘edge’ represents the medial axis of the hallway polygons, which physically connects these rooms. Similar node-links should be applied stairs and elevators to connect building floors. To generate this model, I selected the campus of University of Saskatchewan as the study area and presented a method that creates an indoor navigation network using ESRI ArcGIS products. First, the proposed method automatically extracts geometry and topology of campus buildings and computes the distances among all entities to calculate the shortest path between them. The system navigates through the University campus and it helps locating classrooms, offices,or facilities. The calculation of the route is based on the Dijkstra algorithm, but could employ any network navigation algorithm. To show the advantage of the generated network, I present results of a study conducted in conjunction with the department of Computer Science. An experiment that included 37 participants was designed to collect the tracking data on a university campus to demonstrate how the incorporation of the indoor navigation model can improve the analysis of the indoor movement data. Based on the results of the study it can be concluded that the generated indoor network can be applied to raw positioning data in order to improve accuracy as well as be employed as a stand-alone tool for enhancing of the route guidance on a university campus, and by extension any large indoor space consisting of individual or multiple buildings.

1.       引言

2. 研究方法：生成可行走中心线的导航网络

3. 结果：可行走中心线导航网络

4. WCN的应用：定位与行走路线实验

5. 结论

6. 参考文献

更多精彩文章请关注公众号：