【摘要】：如今,隨著智能手機(jī)的迅速普及,高精度的室內(nèi)定位服務(wù)正在快速的發(fā)展,人們可以使用隨身攜帶的智能手機(jī)來(lái)享受定位服務(wù)。一般來(lái)說(shuō),無(wú)論是基于WiFi,藍(lán)牙,zigbee還是視覺(jué)等定位技術(shù),它們的部署都需要提前獲取精確的室內(nèi)地理信息。在精確的室內(nèi)地理信息幫助下,室內(nèi)導(dǎo)航系統(tǒng)可以在離線階段構(gòu)建特定的數(shù)據(jù)庫(kù),并且當(dāng)在線階段返回定位結(jié)果時(shí),能夠在智能手機(jī)中清楚地顯示其位置和導(dǎo)航估計(jì)。但是,如果在未知環(huán)境或未獲得準(zhǔn)確的室內(nèi)建筑圖的情況下,缺少實(shí)現(xiàn)室內(nèi)定位技術(shù)部署的先決條件,因此需要一種低成本的快速建立室內(nèi)地圖技術(shù)。因此針對(duì)在未知室內(nèi)環(huán)境缺少室內(nèi)地圖的問(wèn)題,本文提出了一種眾包室內(nèi)地圖建立方法,實(shí)現(xiàn)了在未知環(huán)境下通過(guò)眾包用戶IMU(Inertial measurement unit)數(shù)據(jù),完成室內(nèi)地圖的快速建立。具體研究?jī)?nèi)容如下:首先針對(duì)PDR(Pedestrian Dead Reckoning)算法在眾包測(cè)量中精確性較差的問(wèn)題,提出了一種基于PDR算法的眾包用戶姿勢(shì)識(shí)別與軌跡起始點(diǎn)檢測(cè)算法,該算法能夠有效的在不同測(cè)量模式下生成眾包PDR軌跡,并進(jìn)一步提升軌跡精度。其次,針對(duì)室內(nèi)用戶軌跡分布缺少統(tǒng)一模型的問(wèn)題,根據(jù)室內(nèi)用戶行走習(xí)慣,建立了一種室內(nèi)環(huán)境用戶行走模型,該模型能夠有效的描述用戶在室內(nèi)行走習(xí)慣并給出PDR軌跡的概率分布。另外,本文通過(guò)一種基于密度峰值的聚類算法對(duì)長(zhǎng)直走廊數(shù)據(jù)進(jìn)行聚類,并根據(jù)3?原理,計(jì)算得到每段長(zhǎng)直走廊對(duì)應(yīng)的走廊寬度,獲取了室內(nèi)地圖建立的重要參數(shù)。最后,針對(duì)室內(nèi)眾包用戶軌跡構(gòu)建室內(nèi)地圖時(shí)存在PDR誤差與眾包誤差的問(wèn)題,本文基于室內(nèi)行人行走模型,提出了一種基于軌跡密度分析的室內(nèi)地圖建立方法,該方法能夠有效的在未知環(huán)境下構(gòu)建室內(nèi)地圖。該方法包括對(duì)熱點(diǎn)區(qū)域劃分,軌跡密度計(jì)算,熱點(diǎn)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(duì)保留的熱點(diǎn)區(qū)域進(jìn)行邊緣提取,得到室內(nèi)地圖輪廓。最終通過(guò)求解得到的走廊寬度進(jìn)行輪廓的直線化生成,得到精確的室內(nèi)地圖。通過(guò)與真實(shí)環(huán)境室內(nèi)地圖對(duì)比,本文提出的基于眾包IMU數(shù)據(jù)的室內(nèi)地圖建立方法能在保證精度的條件下,實(shí)現(xiàn)室內(nèi)地圖的低成本快速建立。從而能夠在未知環(huán)境下實(shí)現(xiàn)快速獲取室內(nèi)地理信息,擴(kuò)展了室內(nèi)定位的應(yīng)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(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN96

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 陳國(guó)良;李飛;張言哲;;一種基于自適應(yīng)波峰檢測(cè)的MEMS計(jì)步算法[J];中國(guó)慣性技術(shù)學(xué)報(bào);2015年03期

2 關(guān)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(shí)現(xiàn)[J];電腦編程技巧與維護(hù);2015年09期

3 陳興秀;張金藝;晏理;劉江;周文強(qiáng);;三維復(fù)雜運(yùn)動(dòng)模式航跡推算慣性導(dǎo)航室內(nèi)定位[J];應(yīng)用科學(xué)學(xué)報(bào);2014年04期

4 錢小敏;陳斌;;室內(nèi)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(qiáng);薛志偉;齊曉飛;林蓉;;室內(nèi)地圖研究綜述[J];地礦測(cè)繪;2012年02期

6 周飛;;利用Alpha Shapes算法提取離散點(diǎn)輪廓線[J];湖北廣播電視大學(xué)學(xué)報(bào);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(dǎo)航系統(tǒng)的姿態(tài)解算[J];光學(xué)精密工程;2008年10期

8 沈蔚;李京;陳云浩;鄧?yán)?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(xué)報(bào);2008年05期

相關(guān)博士學(xué)位論文 前1條

1 陳偉;基于GPS和自包含傳感器的行人室內(nèi)外無(wú)縫定位算法研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2010年

相關(guān)碩士學(xué)位論文 前6條

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

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

3 張儷文;基于運(yùn)動(dòng)傳感器的行人航跡推算方法研究[D];中國(guó)礦業(yè)大學(xué);2015年

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

5 齊保振;基于運(yùn)動(dòng)傳感的個(gè)人導(dǎo)航系統(tǒng)及算法研究[D];浙江大學(xué);2013年

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

，

本文編號(hào)：2476521