本文選題：LiDAR + 點云濾波　； 參考：《西南交通大學》2014年碩士論文

【摘要】：機載激光雷達系統(tǒng)是將激光測距(LDM)、全球定位系統(tǒng)(GPS)和慣性導航系統(tǒng)(INS)等三種技術集于一身的測量系統(tǒng),它被用于獲得地表的三維數(shù)據(jù)。利用直接采集獲取的激光點云數(shù)據(jù)結(jié)合其對應的數(shù)碼影像數(shù)據(jù),經(jīng)加工處理后便可以得到DEM、 DOM、DTM和DSM等多種豐富的數(shù)據(jù)產(chǎn)品。點云數(shù)據(jù)的濾波指的是從點云中分離出地面點集,這一過程是數(shù)據(jù)后處理中重要的組成部分,也是相關數(shù)據(jù)產(chǎn)品生產(chǎn)的基礎。隨著系統(tǒng)硬件的發(fā)展和人們對精度與準度的不斷要求,濾波算法正面臨著海量數(shù)據(jù)、多變的地形和復雜的地物等的挑戰(zhàn)。在ISPRS于2003年對經(jīng)典濾波算法測試分析之后,相繼出現(xiàn)了不少新的濾波思路和方法改進,也有很多的研究和發(fā)展成果,但是閾值難以確定和對復雜多變的測區(qū)點云穩(wěn)定性差仍然是目前大部分濾波算法存在的主要問題。因此,對真正能夠準確、高效、適應性強的算法的研究是具有一定的價值和意義的。正是針對以上兩點問題,本文通過研究總結(jié)現(xiàn)有濾波算法,改進Bartels等的偏度平衡算法,并利用ISPRS提供的參考數(shù)據(jù)樣本進行實驗分析。主要開展了以下工作： (1)研究了基于高程信息偏度平衡的濾波算法,歸納了國內(nèi)外學者對該算法的相關改進,總結(jié)了各種改進后仍然存在的問題,并針對樣本統(tǒng)計量的可靠性對點數(shù)的影響、平衡的終止條件難以滿足、起伏地區(qū)迭代算法的適用性、缺少地形細節(jié)處理等四點不足,并針對這些不足提出了一種改進的算法,通過,對“起伏地區(qū)點云”將由高到低取點改為從低到高取點、將終止條件由sk≤0改為ske(某個很小的數(shù)),增強了算法對“起伏地區(qū)點云”處理的可靠性、收斂速度和適應性。 (2)總結(jié)了經(jīng)典濾波算法的設計思路和對經(jīng)典算法的相關改進。重點研究了基于TIN的逐漸加密算法,針對偏度平衡算法低矮地物難以濾除、地形結(jié)構(gòu)特征被破壞的兩點不足,進行了D-P算法地形特征點線提取、四叉樹格網(wǎng)劃分、地形特征點線約束下的種子點選取、添加點云邊界輔助點、優(yōu)先加密非特征格網(wǎng)和待定點排序等改進,增強了算法對待定點的判斷準確性、對低矮地物的濾除效果和在處理“起伏地區(qū)點云”時保護地形特征的能力。 (3)選擇了兩塊典型的點云數(shù)據(jù),分別代表著平坦地區(qū)點云和起伏地區(qū)點云,利用改進后的算法進行濾波實驗,將濾波結(jié)果與單獨利用兩種原方法的結(jié)果進行對比,從兩類誤差的角度評價了改進后算法的質(zhì)量。 實驗表明,本文提出的濾波算法是可行的,它結(jié)合了偏度平衡與TIN加密的各自的優(yōu)勢。其中,平坦類點云通過偏度平衡算法從上往下劃層后,使用TIN的逐漸加密算法對低矮地物的濾除有著較好的效果：而起伏類點云是從下往上依次劃層,使每層數(shù)據(jù)是由低向高加密到TIN,并通過等高線分析提取地形特征點,對被加密的初始TIN進行約束,使得算法在較強閾值情況下既不會破壞地形結(jié)構(gòu)也能濾除各個高程范圍內(nèi)的低矮地物。兩類誤差的分析表明,本文算法在在兩類點云的濾波上均能很好地控制兩類誤差,并在控制第二類誤差的前提下減少了第一類誤差。
[Abstract]:Airborne laser radar system is the laser ranging (LDM), global positioning system (GPS) and inertial navigation system (INS) and other three kinds of technology in a measurement system, which is used for 3D surface data obtained. Laser point cloud data obtained by direct acquisition of its combination of digital image data should be. After processing can be DEM, DOM, DTM and DSM and other products. The rich data point cloud data filtering refers to the separation of the ground points set from point cloud data, this process is the important part in the postprocessing, is based on the data related to product production. With the development of hardware system and there have been calls on the accuracy and precision of the algorithm, is facing the challenge of massive data, changing the topography and complex features such as ISPRS in 2003. After the analysis of the classical filtering algorithm testing, there have been a lot of new filter Improvement ideas and methods, there are a lot of research and development results, but it is difficult to determine the threshold of survey area and point cloud stability of complex difference is still the main problem at present most filtering algorithm exists. Therefore, to really be able to accurately and efficiently, the research of adaptable algorithms is has a certain value and significance. It is for the above two problems, this paper summarizes the existing filtering algorithm, improved Bartels algorithm of skewness balancing of the sample and reference data provided by ISPRS are analyzed. The main work carried out the following:
(1) the elevation information filtering algorithm based on the balance of skewness, sums up the relevant domestic and foreign scholars on the improvement of the algorithm, summarizes the various improved problems and affects the reliability of the sample statistics on the number of the balance, the termination conditions are difficult to meet, the applicability of the ups and Downs Area iterative algorithm, the lack of terrain details processing four points, for these problems and proposes an improved algorithm, through to "ups and downs area point cloud" will be from high to low point changed from low to high points, the termination condition by SK = 0 to ske (a very small number). To enhance the algorithm on the "ups and downs area reliability point cloud processing, convergence speed and adaptability.
(2) summarizes the design ideas of classical filtering algorithm and improvement of the classical algorithm. Focus on the gradual encryption algorithm based on TIN, the skewness balancing algorithm is difficult to filter out low ground, two topography characteristics destroyed, the D-P algorithm for terrain feature points extraction, four fork tree grid. The seed point terrain feature points under the constraints of line selection, adding point cloud boundary auxiliary point, non priority encryption features of grid and fixed point ranking improved, enhanced the accuracy of the algorithm is to point to the low ground, filtering effect and protection of terrain features in the handling of the "ups and downs area point cloud" ability.
(3) chose two typical point cloud data, representing a flat area point cloud and downs area point cloud filtering experiment using the improved algorithm, comparing the filtering results and separately using two kinds of original method results from two kinds of error to evaluate the improved the quality of the algorithm.
Experimental results show that the algorithm proposed in this paper is feasible, it combines the skewness balancing and TIN encryption of their respective advantages. Among them, the flat point cloud by skewness balancing algorithm from the top row layer, using TIN encryption algorithm to filter out gradually low ground has a good effect: the ups and downs of point cloud from the bottom up in order to draw layer, each layer of data is from low to high encryption to TIN, and the extraction of terrain feature points through contour analysis, constraints on the initial TIN is encrypted, which makes the algorithm in strong threshold conditions without destroying the structure of terrain can also filter out each elevation within the scope of the low ground analysis. Two kinds of error shows that this algorithm can well control the two types of errors in two kinds of point cloud filtering, and reduces the error in the first premise to control second errors.

【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：P237;P225

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