基于眾包的室內(nèi)地圖自動構(gòu)建方法研究與實現(xiàn)
發(fā)布時間：2019-01-28 21:25
【摘要】：基于位置的服務(wù)和應(yīng)用,如基于位置的社交網(wǎng)絡(luò)、游戲和廣告等,在過去十多年里得到了快速增長。這得力于智能移動設(shè)備的普遍使用和定位技術(shù)的發(fā)展。這些基于位置的服務(wù)和應(yīng)用通常使用地圖來顯示用戶位置。戶外位置服務(wù)提供商提供了幾乎所有地區(qū)的室外街道地圖,但室內(nèi)平面圖的發(fā)展目前仍非常有限,極大影響了基于室內(nèi)定位的服務(wù)和應(yīng)用的快速發(fā)展和部署。目前絕大多數(shù)室內(nèi)應(yīng)用都依賴于手動創(chuàng)建的室內(nèi)平面圖。手動添加、編輯和維護大量的建筑物平面圖需要巨大的成本和努力。為解決上述問題,本文提出一種基于智能手機和眾包方式的室內(nèi)地圖自動構(gòu)建方法。該方法通過手機傳感器采集人在室內(nèi)行動的相關(guān)數(shù)據(jù),通過行人航位推算算法計算得到人的步數(shù)、步長和方向,從而確定人在每一步的位置信息,并由此獲得人的行走軌跡。為了準(zhǔn)確高效構(gòu)建地圖,本文采用眾包方式獲取大量行人的軌跡信息,通過對大量軌跡數(shù)據(jù)的分析,構(gòu)建出室內(nèi)地圖信息。為了檢測室內(nèi)標(biāo)志點,本文提出一種有效的上下樓位置檢測算法和房間門口位置檢測算法。上下樓檢測基于氣壓數(shù)據(jù)斜率變化和加速度量級。房間門口位置檢測結(jié)合陀螺儀和WiFi進行,并通過基于密度的聚類算法進一步確定門口位置。根據(jù)門口位置,本文對大量的行人軌跡數(shù)據(jù)進行分段和聚類處理。對房間類型軌跡,本文使用?-shape算法構(gòu)建出房間形狀和大小。對走廊類型軌跡,本文采用主成分分析算法構(gòu)建出走廊的長和寬。根據(jù)軌跡的位置坐標(biāo),將構(gòu)建出的房間和走廊拼接成完整的室內(nèi)地圖,從而完成室內(nèi)地圖自動構(gòu)建。本文對該方法進行了實現(xiàn),并在實際環(huán)境中進行了實驗。實驗結(jié)果顯示,在具有多房間的室內(nèi)地圖自動構(gòu)建實驗中,房間位置的平均誤差為1.96m,走廊長寬的平均誤差為1.85m。本方法構(gòu)建的地圖基本能反應(yīng)真實地圖中各個房間和走廊的相對位置關(guān)系,以及房間在走廊上的位置順序。本文提出的基于眾包的室內(nèi)地圖自動構(gòu)建方法切實可行,實現(xiàn)了室內(nèi)地圖的自動構(gòu)建。
[Abstract]:Location-based services and applications, such as location-based social networks, games and advertising, have grown rapidly over the past decade. This is due to the widespread use of intelligent mobile devices and the development of positioning technology. These location-based services and applications typically use maps to display user locations. Outdoor location service providers provide outdoor street maps in almost all areas, but the development of indoor plans is still very limited, which greatly affects the rapid development and deployment of indoor location-based services and applications. At present, most indoor applications rely on the manual creation of indoor plans. Manually adding, editing, and maintaining a large number of building plans requires enormous cost and effort. In order to solve the above problems, this paper presents an automatic building method of indoor map based on smart phone and crowdsourcing. In this method, the mobile phone sensor is used to collect the relative data of human movement in the room, and the pedestrian path calculation algorithm is used to calculate the step number, step size and direction of the person, so as to determine the position information of the person at each step, and thus obtain the human walking track. In order to construct the map accurately and efficiently, this paper uses crowdsourcing method to obtain a large number of pedestrian track information, through the analysis of a large number of track data, the indoor map information is constructed. In order to detect indoor markers, this paper presents an effective algorithm for detecting the position of the upper and lower floors and the location of the door of the room. The upper and lower building detects the magnitude of slope and acceleration based on air pressure data. The door position detection is carried out with gyroscope and WiFi, and the location of the door is further determined by density-based clustering algorithm. According to the location of the doorway, this paper deals with a large number of pedestrian trajectory data segmentation and clustering. This paper uses the?-shape algorithm to construct the shape and size of the room. In this paper, the length and width of corridor are constructed by principal component analysis (PCA) algorithm. According to the position coordinate of the track, the room and corridor are assembled into a complete indoor map, and the indoor map is constructed automatically. In this paper, the method is implemented, and the experiment is carried out in the actual environment. The experimental results show that the average error of room position is 1.96 m and the average error of corridor length and width is 1.85 m in the experiment of automatic building indoor map with multiple rooms. The map constructed by this method can basically reflect the relative position relationship of each room and corridor in the real map, as well as the location order of the room on the corridor. The automatic building method of indoor map based on crowdsourcing is feasible, and the automatic construction of indoor map is realized.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P283.7
本文編號：2417332
[Abstract]:Location-based services and applications, such as location-based social networks, games and advertising, have grown rapidly over the past decade. This is due to the widespread use of intelligent mobile devices and the development of positioning technology. These location-based services and applications typically use maps to display user locations. Outdoor location service providers provide outdoor street maps in almost all areas, but the development of indoor plans is still very limited, which greatly affects the rapid development and deployment of indoor location-based services and applications. At present, most indoor applications rely on the manual creation of indoor plans. Manually adding, editing, and maintaining a large number of building plans requires enormous cost and effort. In order to solve the above problems, this paper presents an automatic building method of indoor map based on smart phone and crowdsourcing. In this method, the mobile phone sensor is used to collect the relative data of human movement in the room, and the pedestrian path calculation algorithm is used to calculate the step number, step size and direction of the person, so as to determine the position information of the person at each step, and thus obtain the human walking track. In order to construct the map accurately and efficiently, this paper uses crowdsourcing method to obtain a large number of pedestrian track information, through the analysis of a large number of track data, the indoor map information is constructed. In order to detect indoor markers, this paper presents an effective algorithm for detecting the position of the upper and lower floors and the location of the door of the room. The upper and lower building detects the magnitude of slope and acceleration based on air pressure data. The door position detection is carried out with gyroscope and WiFi, and the location of the door is further determined by density-based clustering algorithm. According to the location of the doorway, this paper deals with a large number of pedestrian trajectory data segmentation and clustering. This paper uses the?-shape algorithm to construct the shape and size of the room. In this paper, the length and width of corridor are constructed by principal component analysis (PCA) algorithm. According to the position coordinate of the track, the room and corridor are assembled into a complete indoor map, and the indoor map is constructed automatically. In this paper, the method is implemented, and the experiment is carried out in the actual environment. The experimental results show that the average error of room position is 1.96 m and the average error of corridor length and width is 1.85 m in the experiment of automatic building indoor map with multiple rooms. The map constructed by this method can basically reflect the relative position relationship of each room and corridor in the real map, as well as the location order of the room on the corridor. The automatic building method of indoor map based on crowdsourcing is feasible, and the automatic construction of indoor map is realized.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P283.7
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