本文選題：國產(chǎn)LiDAR + 系統(tǒng)檢校��； 參考：《昆明理工大學》2017年碩士論文

【摘要】：機載激光雷達作為一種快速、實時、高效的獲取地理空間信息的新型技術(shù),對傳統(tǒng)測繪事業(yè)的發(fā)展起到了巨大的推動作用。本文圍繞著國產(chǎn)機載LiDAR系統(tǒng)AP-3500的定位模型建立,集成誤差檢校,獲取的點云與影像融合等方面進行研究,以提高點云定位精度,并通過實驗對所獲取點云的精度進行驗證。本文完成的主要工作如下:(1)系統(tǒng)的集成研究及誤差修正模型建立。對系統(tǒng)的硬件設(shè)備組成及工作原理做了簡要介紹,并闡述了慣性導(dǎo)航系統(tǒng)的定位原理與LiDAR點云的測量原理,同時,為了得到三維激光腳點精確的三維坐標,在系統(tǒng)各部件之間建立相對獨立的坐標系,通過研究建立起了消除誤差的數(shù)學模型,找到了各坐標系間的位置與角度偏差。(2)集成誤差檢校。建立起了解求單相機的畸變系數(shù)與內(nèi)方外元素的檢校方式,并完成了雙拼相機的平臺檢校,且通過對激光掃描儀的測距測角誤差進行檢校,建立起了掃描儀的檢校方案,同時完成了 POS與LiDAR系統(tǒng)及相機系統(tǒng)的集成誤差檢校,構(gòu)建了一整套誤差集成檢校方法。(3)數(shù)據(jù)處理研究。通過實驗,得到了對影像輻射信息損失相對最少,變形相對最小的影像處理流程與方法,且借助影像信息的輔助對點云數(shù)據(jù)進行處理,并成功將兩種數(shù)據(jù)進行融合,使融合后的點云即擁有強度,回波次數(shù),反射強度,三維坐標信息等原有信息,還具備了影像光譜信息,隨后借助這些特征信息對點云進行過濾與分類處理,利用濾波所得的地面點構(gòu)建地面三角網(wǎng),生成高精度DEM。(4)DEM精度驗證與分析。在國家測繪地理信息局相關(guān)科研項目的支撐下,在河南省平頂山市衛(wèi)東區(qū)某地進行飛行試驗,利用同機飛行獲取的影像與點云進行融合操作,并利用融合后的綜合信息進行過濾與分類處理,適當?shù)募右允止じ深A(yù)后,利用處理所得地面點構(gòu)建DEM,并利用在實驗區(qū)放置的正方形標靶的實測中心與擬合中心的坐標差值來驗證點云的平面與高程精度,最后使用檢查點檢測法對DEM進行精度評定。
[Abstract]:Airborne lidar, as a new technology to obtain geospatial information quickly, real-time and efficiently, has played a great role in promoting the development of traditional surveying and mapping. In order to improve the accuracy of point cloud location, this paper focuses on the establishment of the localization model of the domestic airborne LiDAR system AP-3500, the integration error checking, the fusion of the obtained point cloud and the image, and verifies the accuracy of the obtained point cloud through experiments. The main work of this paper is as follows: 1) Integration of the system and establishment of error correction model. The hardware components and working principle of the system are briefly introduced, and the positioning principle of inertial navigation system and the measuring principle of LiDAR point cloud are expounded. At the same time, in order to obtain the accurate 3D coordinate of 3D laser foot-point, A relatively independent coordinate system is established among the various parts of the system. A mathematical model for eliminating errors is established and the position and angle deviation between the coordinate systems is found. In this paper, the distortion coefficient of single camera and the checking mode of inner and outer elements are established, and the platform check of double camera is completed, and the calibration scheme of scanner is established by checking the ranging and angle error of laser scanner. At the same time, the integrated error checking of POS, LiDAR system and camera system is completed, and a set of error integrated calibration method. Through experiments, the image processing flow and method with the least loss of image radiation information and the smallest deformation are obtained, and the point cloud data is processed with the aid of image information, and the two kinds of data are fused successfully. The fused point cloud has the original information, such as intensity, echo times, reflection intensity, 3D coordinate information and so on. It also has the spectral information of image, and then filters and classifies the point cloud with the help of these characteristic information. The ground triangulation network is constructed by using the ground points obtained by filtering, and high precision DEM.(4)DEM accuracy verification and analysis are generated. Supported by relevant scientific research projects of the State Bureau of surveying and Mapping Geographic Information, a flight test was conducted in the Weidong District of Pingdingshan City, Henan Province. The image obtained from the same flight was fused with the point cloud. The integrated information was filtered and classified, and the appropriate manual intervention was carried out. DEM is constructed by using the treated ground points, and the coordinate difference between the measured center and the fitting center of the square target placed in the experimental area is used to verify the plane and elevation accuracy of the point cloud. Finally, the accuracy of DEM is evaluated by checkpoint detection method.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P23

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