本文選題：激光掃描 + 數(shù)據(jù)解譯　； 參考：《武漢理工大學(xué)》2013年碩士論文

【摘要】：激光掃描測量技術(shù)是繼GPS之后測繪領(lǐng)域誕生的一種高新測繪技術(shù),能夠快速高效地獲取目標(biāo)物體表面詳細(xì)的三維空間信息,在數(shù)字城市、環(huán)境監(jiān)測、交通仿真等領(lǐng)域為快速實現(xiàn)三維建模提供了一種全新的技術(shù)手段。因此,如何有效地處理激光掃描數(shù)據(jù)具有實際的應(yīng)用意義。在此背景下,本文以校園真實環(huán)境作為實驗場景,以車載激光掃描系統(tǒng)作為三維信息的獲取方式,研究車載激光掃描數(shù)據(jù)的處理,重點(diǎn)解決激光點(diǎn)云的分類問題和移動目標(biāo)的特征提取問題。 圍繞上述研究目標(biāo),本文主要做了以下三個方面的工作： 1.進(jìn)行車載校園激光數(shù)據(jù)的實地采集和預(yù)處理 選擇了特定的校園場景,使用新型的HDL-64E S2三維激光掃描儀進(jìn)行了實驗數(shù)據(jù)的實地采集。車載校園激光數(shù)據(jù)的預(yù)處理包括原始激光數(shù)據(jù)的解譯、激光點(diǎn)云的精簡和激光點(diǎn)云的三維可視化三方面的工作。通過激光數(shù)據(jù)的預(yù)處理才能獲得后處理階段所需的“點(diǎn)云”。 2.進(jìn)行車載激光點(diǎn)云的實際分類 激光點(diǎn)云分類是利用一些可行的分類策略,將海量獨(dú)立的空間點(diǎn)劃分到一系列具有實際物理意義的類簇里,使離散的獨(dú)立點(diǎn)具有實際的物理意義。在分類過程中,本文先對比分析了三種現(xiàn)有的分類策略的優(yōu)劣,然后選擇垂直高度作為主要分類因素、投影點(diǎn)密度作為次要分類因素,提出了一個改進(jìn)的基于垂直高度的雙閾值法進(jìn)行激光點(diǎn)云的分類,該方法的核心是基于網(wǎng)格的統(tǒng)計分析。 3.進(jìn)行移動目標(biāo)的特征提取和目標(biāo)個體的識別 移動目標(biāo)(行人和車輛)的特征提取和目標(biāo)個體的識別是在點(diǎn)云分類的基礎(chǔ)(結(jié)果集)上進(jìn)行的。鑒于激光點(diǎn)的高度離散性和抽象性,很難直接從中提取地物的點(diǎn)、線、面特征,本文采用了間接的處理方式,提出了一種基于圖像處理技術(shù)的移動目標(biāo)特征提取及識別方法：利用水平網(wǎng)格進(jìn)行投影,將三維激光點(diǎn)云降維轉(zhuǎn)換得到二維二值灰度圖像,然后運(yùn)用圖像處理技術(shù)進(jìn)行相關(guān)的特征提取,再根據(jù)提取的特征進(jìn)行目標(biāo)個體的識別。 運(yùn)用本文所提出的點(diǎn)云分類方法和特征提取方法,較好地實現(xiàn)了車載校園激光點(diǎn)云的分類、移動目標(biāo)的特征提取和目標(biāo)個體的識別。
[Abstract]:Laser scanning measurement technology is a new technology of surveying and mapping , which is born in the field of surveying and mapping after GPS . It provides a brand - new technical means for quickly and efficiently acquiring the detailed three - dimensional space information of the target object surface . In this background , the paper studies the processing of vehicle - mounted laser scanning data in the field of digital city , environment monitoring , traffic simulation and so on , which focuses on the classification of laser spot clouds and the feature extraction of moving targets .


Based on the above research objectives , the thesis mainly focuses on the following three aspects :


1 . On - site collection and preprocessing of vehicle - mounted campus laser data


This paper selects a specific campus scene , and uses the new HDL - 64E S2 three - dimensional laser scanner to collect the experimental data . The preprocessing of the laser data of the vehicle - mounted campus includes the interpretation of the original laser data , the simplification of the laser point cloud and the three - dimensional visualization of the laser point cloud . The " point cloud " required for the post - processing stage can be obtained by the preprocessing of the laser data .


2 . Actual classification of vehicle - mounted laser point cloud


The classification of laser point cloud is to use some feasible classification strategies to divide the mass independent spatial points into a series of cluster with physical meaning . In the process of classification , this paper compares the advantages and disadvantages of three existing classification strategies , then selects the vertical height as the main classification factor and the projection point density as the secondary classification factor , and proposes an improved double threshold method based on the vertical height for the classification of the laser point cloud .


3 . Feature Extraction of Moving Objects and Recognition of Target Individuals


The feature extraction and recognition of moving objects ( pedestrians and vehicles ) are carried out on the basis of point cloud classification ( the result set ) . In view of the high degree of discreteness and abstraction of laser spots , it is difficult to directly extract the points , lines and surface features of the ground objects . In this paper , a moving target feature extraction and recognition method based on image processing technology is proposed : a horizontal grid is used for projection , the three - dimensional laser point cloud is reduced and converted to a two - dimensional binary grayscale image , then the relevant feature extraction is carried out by using the image processing technique , and the identification of the target individual is carried out according to the extracted features .


By using the point cloud classification method and feature extraction method proposed in this paper , the classification , feature extraction and target individual identification of the vehicle - mounted campus laser point cloud are well realized .
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P225.2;TP391.41

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