發(fā)布時間：2019-05-14 07:05

【摘要】：如今,隨著智能手機的迅速普及,高精度的室內定位服務正在快速的發(fā)展,人們可以使用隨身攜帶的智能手機來享受定位服務。一般來說,無論是基于WiFi,藍牙,zigbee還是視覺等定位技術,它們的部署都需要提前獲取精確的室內地理信息。在精確的室內地理信息幫助下,室內導航系統(tǒng)可以在離線階段構建特定的數(shù)據(jù)庫,并且當在線階段返回定位結果時,能夠在智能手機中清楚地顯示其位置和導航估計。但是,如果在未知環(huán)境或未獲得準確的室內建筑圖的情況下,缺少實現(xiàn)室內定位技術部署的先決條件,因此需要一種低成本的快速建立室內地圖技術。因此針對在未知室內環(huán)境缺少室內地圖的問題,本文提出了一種眾包室內地圖建立方法,實現(xiàn)了在未知環(huán)境下通過眾包用戶IMU(Inertial measurement unit)數(shù)據(jù),完成室內地圖的快速建立。具體研究內容如下:首先針對PDR(Pedestrian Dead Reckoning)算法在眾包測量中精確性較差的問題,提出了一種基于PDR算法的眾包用戶姿勢識別與軌跡起始點檢測算法,該算法能夠有效的在不同測量模式下生成眾包PDR軌跡,并進一步提升軌跡精度。其次,針對室內用戶軌跡分布缺少統(tǒng)一模型的問題,根據(jù)室內用戶行走習慣,建立了一種室內環(huán)境用戶行走模型,該模型能夠有效的描述用戶在室內行走習慣并給出PDR軌跡的概率分布。另外,本文通過一種基于密度峰值的聚類算法對長直走廊數(shù)據(jù)進行聚類,并根據(jù)3?原理,計算得到每段長直走廊對應的走廊寬度,獲取了室內地圖建立的重要參數(shù)。最后,針對室內眾包用戶軌跡構建室內地圖時存在PDR誤差與眾包誤差的問題,本文基于室內行人行走模型,提出了一種基于軌跡密度分析的室內地圖建立方法,該方法能夠有效的在未知環(huán)境下構建室內地圖。該方法包括對熱點區(qū)域劃分,軌跡密度計算,熱點區(qū)域篩選,地圖輪廓生成與直線化地圖生成。本文利用Alpha-shape算法對保留的熱點區(qū)域進行邊緣提取,得到室內地圖輪廓。最終通過求解得到的走廊寬度進行輪廓的直線化生成,得到精確的室內地圖。通過與真實環(huán)境室內地圖對比,本文提出的基于眾包IMU數(shù)據(jù)的室內地圖建立方法能在保證精度的條件下,實現(xiàn)室內地圖的低成本快速建立。從而能夠在未知環(huán)境下實現(xiàn)快速獲取室內地理信息,擴展了室內定位的應用前景。
[Abstract]:Nowadays, with the rapid popularity of smartphones, high-precision indoor positioning services are developing rapidly, people can use portable smartphones to enjoy positioning services. Generally speaking, whether based on WiFi, Bluetooth, zigbee or visual positioning technology, their deployment needs to obtain accurate indoor geographic information in advance. With the help of accurate indoor geographic information, the indoor navigation system can build a specific database in the offline phase, and when the positioning results are returned in the online phase, the location and navigation estimates can be clearly displayed in the smartphone. However, if the environment is unknown or the accurate indoor building map is not obtained, there is no prerequisite for the deployment of indoor positioning technology, so it is necessary to establish indoor map technology quickly and cheaply. Therefore, in order to solve the problem of lack of indoor map in unknown indoor environment, this paper proposes a method of building indoor map in crowdsourcing, which realizes the rapid establishment of indoor map through crowdsourcing user IMU (Inertial measurement unit) data in unknown environment. The specific research contents are as follows: firstly, in order to solve the problem of poor accuracy of PDR (Pedestrian Dead Reckoning) algorithm in crowdsourcing measurement, a crowdsourcing user posture recognition and trajectory starting point detection algorithm based on PDR algorithm is proposed. The algorithm can effectively generate crowdsourcing PDR trajectories in different measurement modes, and further improve the trajectory accuracy. Secondly, according to the walking habits of indoor users, a kind of indoor environment user walking model is established to solve the problem of lack of unified model for indoor user trajectory distribution. The model can effectively describe the indoor walking habits of users and give the probability distribution of PDR trajectory. In addition, this paper uses a clustering algorithm based on density peak value to cluster the long straight corridor data, and according to 3? In principle, the corridor width corresponding to each long straight corridor is calculated, and the important parameters of indoor map establishment are obtained. Finally, in order to solve the problem of PDR error and crowdsourcing error when indoor crowdsourcing user trajectory is constructed, based on indoor pedestrian walking model, an indoor map establishment method based on trajectory density analysis is proposed in this paper. This method can effectively construct indoor map in unknown environment. The method includes the division of hot spots, the calculation of trajectory density, the screening of hot spots, the generation of map contours and the generation of straight maps. In this paper, Alpha-shape algorithm is used to extract the edge of the reserved hot spot, and the outline of indoor map is obtained. Finally, by solving the width of the corridor, the outline is straightened and the accurate indoor map is obtained. Compared with the real environment indoor map, the indoor map establishment method based on crowdsourcing IMU data can realize the low cost and fast establishment of indoor map under the condition of ensuring the accuracy. Thus, the indoor geographic information can be obtained quickly in unknown environment, and the application prospect of indoor positioning can be expanded.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN96

【參考文獻】

相關期刊論文 前8條

1 陳國良;李飛;張言哲;;一種基于自適應波峰檢測的MEMS計步算法[J];中國慣性技術學報;2015年03期

2 關越魏;何波賢;于仁清;丁廣威;;基于四元數(shù)解算陀螺儀姿態(tài)角算法的實現(xiàn)[J];電腦編程技巧與維護;2015年09期

3 陳興秀;張金藝;晏理;劉江;周文強;;三維復雜運動模式航跡推算慣性導航室內定位[J];應用科學學報;2014年04期

4 錢小敏;陳斌;;室內GIS的研究現(xiàn)狀與前景[J];地理空間信息;2012年04期

5 王富強;薛志偉;齊曉飛;林蓉;;室內地圖研究綜述[J];地礦測繪;2012年02期

6 周飛;;利用Alpha Shapes算法提取離散點輪廓線[J];湖北廣播電視大學學報;2010年02期

7 張榮輝;賈宏光;陳濤;張躍;;基于四元數(shù)法的捷聯(lián)式慣性導航系統(tǒng)的姿態(tài)解算[J];光學精密工程;2008年10期

8 沈蔚;李京;陳云浩;鄧磊;彭光雄;;基于LIDAR數(shù)據(jù)的建筑輪廓線提取及規(guī)則化算法研究[J];遙感學報;2008年05期

相關博士學位論文 前1條

1 陳偉;基于GPS和自包含傳感器的行人室內外無縫定位算法研究[D];中國科學技術大學;2010年

相關碩士學位論文 前6條

1 蘇邵麟;基于手機傳感器和地標的Radio Map建立算法研究[D];哈爾濱工業(yè)大學;2016年

2 陳偉;基于四元數(shù)和卡爾曼濾波的姿態(tài)角估計算法研究與應用[D];燕山大學;2015年

3 張儷文;基于運動傳感器的行人航跡推算方法研究[D];中國礦業(yè)大學;2015年

4 殷紅;基于foot-mounted的IMU室內行人航跡推算研究[D];南昌大學;2013年

5 齊保振;基于運動傳感的個人導航系統(tǒng)及算法研究[D];浙江大學;2013年

6 姜朋;基于MEMS-IMU的捷聯(lián)式慣性導航系統(tǒng)技術與實現(xiàn)研究[D];哈爾濱工程大學;2012年

，

本文編號：2476521