基于車載激光掃描數(shù)據(jù)的高速公路道路要素提取方法研究
發(fā)布時間：2018-08-17 14:30
【摘要】：隨著智慧城市的飛速發(fā)展,市場對數(shù)據(jù)采集設備從采集速度、數(shù)據(jù)精度、處理效率、受干擾因素等方面提出了新的要求,車載激光掃描技術因此成為人們研究的熱點。車載激光掃描技術具有非接觸、高精度、高速度、大范圍、成果直觀的特點,已經(jīng)開始應用在城市規(guī)劃、道路鐵路檢查等領域。雖然車載激光掃描技術在理論上能提供足夠的商業(yè)價值,但因其成本高昂,數(shù)據(jù)成果巨大、數(shù)據(jù)分析和應用效率低等因素,導致實際的利用率較低。本研究從市場需求出發(fā),主要研究自動、高效、準確的從高速公路的車載激光掃描數(shù)據(jù)中提取出道路要素的方法,從而促進車載激光掃描技術的發(fā)展和普及。本文簡要介紹了激光雷達技術的發(fā)展,以及在激光雷達技術基礎上發(fā)展起來的車載激光掃描技術,總結了當前車載激光掃描技術中系統(tǒng)集成、成果數(shù)據(jù)組織與管理、成果數(shù)據(jù)分析和應用等研究熱點的發(fā)展現(xiàn)狀。在這些研究基礎上,依托現(xiàn)有的設備資源,集成一套車載激光掃描系統(tǒng),分析其工作原理和成果誤差來源,并設計實驗對造成誤差的主要參數(shù)進行了標定,最后分析了整個系統(tǒng)的成果點云數(shù)據(jù)的精度。確定成果點云精度改善達到具體的應用要求后,采集高速公路的車載激光系統(tǒng)點云數(shù)據(jù),對數(shù)據(jù)進行分析,提出一套自動、高效、準確的提取高速公路道路要素點云的新方法,為實現(xiàn)高速公路規(guī)劃和養(yǎng)護、公路路面和附屬設施普查提供便利。本文提出的高速公路道路要素提取方法,簡述如下:對于路面的提取,首先通過點采集時間間隔提取每條掃描線,基于車載激光掃描系統(tǒng)的集成原理得到掃描線上各點采集時刻的掃描儀發(fā)射中心投影下來正對的路面點坐標,稱為路面軌跡點。通過分析掃描線上各點與對應采集時刻的路面軌跡點坐標之間的斜率提取出最靠近車載激光掃描系統(tǒng)的路面點,稱為掃描線上的路面起始點。按點采集順序分析從起始點開始掃計算描線上各相鄰點的斜率和斜率差,查找到顯著變化的位置,認為是路面邊界點疑惑點,通過分析前后點斜率和斜率差的方法進行確認,最終提取出掃描線上的路面邊界點。將掃描線上路面起始點和邊界點之間的點作為最終的路面點云。對于道路標線的提取,是對已經(jīng)提取出來的路面點云的每條掃描線,通過反射率濾波進行初提取,再通過基于4倍點間距的三維格網(wǎng)劃分去除離群點的方法進行提取的。對于護欄的提取,首先利用掃描線上的路面邊界點與護欄相鄰的特點初步分割出帶有雜點的護欄點云,由于護欄點云每條掃描線上的主體目標比較集中、雜點相對離散,采用投影到不同高度水平面上提取點狀和線狀密集區(qū)域的方式分別提取出護欄的支撐主柱和波形護欄板點云,最后合并一處。對于路燈的提取,首先利用掃描線上的路面邊界點與路燈相鄰的特點初步分割出帶有雜點的路燈桿狀部分點云,再通過提取路面上方空間點云的方法提取出路燈的燈頭部分點云,合并一處作為路燈點云初步分割的結果。基于初步分割出的結果中主體目標分布相對獨立的特點,采用聚類劃分超級體素,計算體素特征并合并軸向相近的體素成為目標,通過統(tǒng)計目標所包含的體素特征選擇同時包含水平面狀和豎直桿狀體素的目標為路燈。本文所提出的方法結合高速公路道路要素的分布特征對原始點云進行初步分割,減少了數(shù)據(jù)處理量。并且結合道路要素的幾何特征對原始點云進行精細提取,保證了提取的準確度。經(jīng)驗證,方法簡單、高效,極大提升了車載激光掃描系統(tǒng)的實用性。
[Abstract]:With the rapid development of Smart City, the market has put forward new requirements for data acquisition equipment in terms of acquisition speed, data accuracy, processing efficiency, interference factors and so on. Vehicle-borne laser scanning technology has become a hot research topic. It has been used in urban planning, road and railway inspection and other fields. Although in theory, laser scanning technology can provide enough commercial value, but because of its high cost, huge data results, low data analysis and application efficiency and other factors, resulting in low actual utilization rate. This paper briefly introduces the development and popularization of the laser scanning technology for vehicles, and the laser scanning technology for vehicles developed on the basis of the laser radar technology, and summarizes the current vehicle laser scanning technology. Based on these researches and relying on the existing equipment resources, a set of vehicle laser scanning system is integrated, its working principle and error sources are analyzed, and the main parameters causing errors are designed and tested. Finally, the accuracy of the point cloud data of the whole system is analyzed. After the improvement of the point cloud accuracy reaches the specific application requirements, the point cloud data of the laser system on the highway are collected and analyzed. A new method of automatic, efficient and accurate extraction of the point cloud of highway road elements is proposed to achieve high accuracy. Highway planning and maintenance, highway pavement and ancillary facilities survey to facilitate. The highway road elements extraction method proposed in this paper, briefly described as follows: For the extraction of the road surface, first through the point acquisition time interval extraction of each scan line, based on the integration principle of the vehicle laser scanning system to obtain the scan line of each point acquisition time. By analyzing the slope between the points on the scanning line and the coordinates of the points on the corresponding collection time, the pavement point nearest to the vehicle laser scanning system is extracted, which is called the pavement starting point on the scanning line. The gradient and slope difference of adjacent points on the scanning line are calculated by starting sweep to find out the position of obvious change, which is regarded as the doubtful point of the pavement boundary point. The pavement boundary points on the scanning line are finally extracted by analyzing the slope and slope difference between the front and back points. Pavement point clouds. For the extraction of road markings, each scan line of the extracted pavement point clouds is extracted by reflectivity filtering, and then the outliers are removed by three-dimensional mesh partition based on four-fold spacing. For the extraction of guardrail, the boundary points and guardrail on the scanning line are first used. Because the main targets on each scan line of the guardrail point cloud are relatively concentrated and the clutter is relatively discrete, the point cloud and the wavy guardrail point cloud of the guardrail are extracted respectively by projecting to the horizontal plane of different heights to extract the point cloud and the linear dense area, and finally merge into one. For the extraction of street lamp, firstly, the pole-shaped part of the street lamp point cloud with miscellaneous points is preliminarily segmented by using the characteristics of the boundary points on the scanning line adjacent to the street lamp. Then the point cloud at the head of the street lamp is extracted by the method of extracting the spatial point cloud above the road surface, and one point is merged as the preliminary segmentation result of the street lamp point cloud. In the cut-out result, the distribution of the main object is relatively independent. Clustering is used to divide the super voxels, computing the voxel features and merging the voxels with similar axial direction to become the target. The target containing both horizontal and vertical bars is selected as the street lamp by statistical voxel features. The distribution characteristics of highway elements can segment the original point cloud and reduce the amount of data processing, and extract the original point cloud precisely according to the geometric features of highway elements to ensure the accuracy of extraction.
【學位授予單位】：北京建筑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;P225.2
本文編號：2187932
[Abstract]:With the rapid development of Smart City, the market has put forward new requirements for data acquisition equipment in terms of acquisition speed, data accuracy, processing efficiency, interference factors and so on. Vehicle-borne laser scanning technology has become a hot research topic. It has been used in urban planning, road and railway inspection and other fields. Although in theory, laser scanning technology can provide enough commercial value, but because of its high cost, huge data results, low data analysis and application efficiency and other factors, resulting in low actual utilization rate. This paper briefly introduces the development and popularization of the laser scanning technology for vehicles, and the laser scanning technology for vehicles developed on the basis of the laser radar technology, and summarizes the current vehicle laser scanning technology. Based on these researches and relying on the existing equipment resources, a set of vehicle laser scanning system is integrated, its working principle and error sources are analyzed, and the main parameters causing errors are designed and tested. Finally, the accuracy of the point cloud data of the whole system is analyzed. After the improvement of the point cloud accuracy reaches the specific application requirements, the point cloud data of the laser system on the highway are collected and analyzed. A new method of automatic, efficient and accurate extraction of the point cloud of highway road elements is proposed to achieve high accuracy. Highway planning and maintenance, highway pavement and ancillary facilities survey to facilitate. The highway road elements extraction method proposed in this paper, briefly described as follows: For the extraction of the road surface, first through the point acquisition time interval extraction of each scan line, based on the integration principle of the vehicle laser scanning system to obtain the scan line of each point acquisition time. By analyzing the slope between the points on the scanning line and the coordinates of the points on the corresponding collection time, the pavement point nearest to the vehicle laser scanning system is extracted, which is called the pavement starting point on the scanning line. The gradient and slope difference of adjacent points on the scanning line are calculated by starting sweep to find out the position of obvious change, which is regarded as the doubtful point of the pavement boundary point. The pavement boundary points on the scanning line are finally extracted by analyzing the slope and slope difference between the front and back points. Pavement point clouds. For the extraction of road markings, each scan line of the extracted pavement point clouds is extracted by reflectivity filtering, and then the outliers are removed by three-dimensional mesh partition based on four-fold spacing. For the extraction of guardrail, the boundary points and guardrail on the scanning line are first used. Because the main targets on each scan line of the guardrail point cloud are relatively concentrated and the clutter is relatively discrete, the point cloud and the wavy guardrail point cloud of the guardrail are extracted respectively by projecting to the horizontal plane of different heights to extract the point cloud and the linear dense area, and finally merge into one. For the extraction of street lamp, firstly, the pole-shaped part of the street lamp point cloud with miscellaneous points is preliminarily segmented by using the characteristics of the boundary points on the scanning line adjacent to the street lamp. Then the point cloud at the head of the street lamp is extracted by the method of extracting the spatial point cloud above the road surface, and one point is merged as the preliminary segmentation result of the street lamp point cloud. In the cut-out result, the distribution of the main object is relatively independent. Clustering is used to divide the super voxels, computing the voxel features and merging the voxels with similar axial direction to become the target. The target containing both horizontal and vertical bars is selected as the street lamp by statistical voxel features. The distribution characteristics of highway elements can segment the original point cloud and reduce the amount of data processing, and extract the original point cloud precisely according to the geometric features of highway elements to ensure the accuracy of extraction.
【學位授予單位】：北京建筑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;P225.2
【參考文獻】
相關期刊論文 前10條
1 董震;楊必勝;;車載激光掃描數(shù)據(jù)中多類目標的層次化提取方法[J];測繪學報;2015年09期
2 田祥瑞;徐立軍;徐騰;李小路;張勤拓;;車載LiDAR掃描系統(tǒng)安置誤差角檢校[J];紅外與激光工程;2014年10期
3 李德仁;姚遠;邵振峰;;智慧城市中的大數(shù)據(jù)[J];武漢大學學報(信息科學版);2014年06期
4 過家春;;子午線弧長公式的簡化及其泰勒級數(shù)解釋[J];測繪學報;2014年02期
5 方莉娜;楊必勝;;車載激光掃描數(shù)據(jù)的結構化道路自動提取方法[J];測繪學報;2013年02期
6 鄒曉亮;繆劍;郭銳增;李星全;趙桂華;;移動車載激光點云的道路標線自動識別與提取[J];測繪與空間地理信息;2012年09期
7 李鑫;李廣云;王力;楊凡;;移動測量系統(tǒng)誤差整體模型推導與精度分析[J];測繪工程;2012年02期
8 譚賁;鐘若飛;李芹;;車載激光掃描數(shù)據(jù)的地物分類方法[J];遙感學報;2012年01期
9 葉澤田;楊勇;趙文吉;侯艷芳;;車載GPS/IMU/LS激光成像系統(tǒng)外方位元素的動態(tài)標定[J];測繪學報;2011年03期
10 賴祖龍;萬幼川;申邵洪;徐景中;;基于Hilbert排列碼與R樹的海量LIDAR點云索引[J];測繪科學;2009年06期
相關博士學位論文 前4條
1 魏征;車載LiDAR點云中建筑物的自動識別與立面幾何重建[D];武漢大學;2012年
2 郭明;海量精細空間數(shù)據(jù)管理技術研究[D];武漢大學;2011年
3 鄒曉亮;車載測量系統(tǒng)數(shù)據(jù)處理若干關鍵技術研究[D];解放軍信息工程大學;2011年
4 孫紅星;差分GPS/INS組合定位定姿及其在MMS中的應用[D];武漢大學;2004年
相關碩士學位論文 前6條
1 魏冠楠;移動LIDAR數(shù)據(jù)采集與預處理方法研究[D];北京建筑大學;2016年
2 夏明波;捷聯(lián)慣性導航系統(tǒng)誤差標定方法研究[D];哈爾濱工業(yè)大學;2015年
3 韋江霞;面向快速建模的車載激光點云的城市典型地物分類方法研究[D];首都師范大學;2014年
4 譚賁;基于車載激光掃描數(shù)據(jù)的城市典型地物分類方法研究[D];首都師范大學;2011年
5 胡競;車載三維激光移動建模系統(tǒng)總體檢校方法研究[D];首都師范大學;2011年
6 張靜;車載平臺姿態(tài)及天線位置確定技術研究[D];山東科技大學;2005年
，本文編號：2187932
本文鏈接：http://sikaile.net/kejilunwen/daoluqiaoliang/2187932.html
