發(fā)布時間：2018-03-05 02:36

  本文選題：環(huán)境感知　切入點：激光雷達數(shù)據(jù)融合　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：無人車在軍事與民用方面具有廣泛的應(yīng)用前景。隨著物聯(lián)網(wǎng)、人工智能、計算機科學(xué)等相關(guān)技術(shù)的發(fā)展,無人車的外在環(huán)境也日臻完善。環(huán)境感知作為無人車系統(tǒng)的重要組成部分,對整個車起著至關(guān)重要的作用。本文針對無人車環(huán)境感知中的兩個重點與難點問題進行研究,設(shè)計了基于雙激光雷達的環(huán)境感知處理架構(gòu),并基于該架構(gòu)研究與實現(xiàn)了結(jié)構(gòu)化環(huán)境下的低矮道邊檢測與非結(jié)構(gòu)化環(huán)境下的負(fù)障礙檢測兩個環(huán)境感知的難點問題。本文的主要研究成果與創(chuàng)新點如下:1、針對以往單個單線、多線激光雷達點云密度小,對于特殊場景感知能力差的特點,研究設(shè)計了基于雙多線激光雷達對稱式安裝的環(huán)境感知與信息融合處理架構(gòu),并對該架構(gòu)下的點云密度進行定量分析。實驗表明,該架構(gòu)大大提高了無人車車體前方觀測區(qū)的點云密度,減小了車體周身盲區(qū),可以解決無人車難度較大的環(huán)境感知問題。2、根據(jù)雙激光雷達水平安裝的雷達掃描特點,分析了雷達點在障礙區(qū)的分布特性,提出一種新的結(jié)構(gòu)化環(huán)境下的低矮道邊的檢測算法。算法使用了基于梯度一致性的點云分割方法,該方法可對雷達點進行快速分割,高效的將雷達點分割為地面點與障礙物點。然后利用路面點與柵格地圖提取出候選道邊點,最后分別使用最小二乘與改進的RANSAC算法進行道邊提取。實驗結(jié)果顯示,點云分割算法具有良好的分割效果,改進后的RANSAC算法具有較高的實時性,能夠滿足無人車的需求。3、針對非結(jié)構(gòu)化環(huán)境下的負(fù)障礙檢測問題提出一種新的感知方法,該方法不依賴于地面平整度,通過局部點云分布特征進行檢測。首先,將雷達點云映射到多尺度柵格,統(tǒng)計各柵格的點云密度與相對高度等特征并做標(biāo)記;然后,從點云數(shù)據(jù)中抽取負(fù)障礙幾何特征,將柵格的統(tǒng)計特征與負(fù)障礙的幾何特征進行多特征關(guān)聯(lián)找到關(guān)鍵特征點對;最后,將特征點對聚類,劃分負(fù)障礙。方法已成功運行在無人車上,實驗表明,該方法具有較高的實時性和可靠性,在非結(jié)構(gòu)化環(huán)境下具有良好的檢測效果。上述研究成果均已成功使用在"行健一號"無人車上,該車多次參加"中國智能車未來挑戰(zhàn)賽",并在比賽中取得優(yōu)異的成績。
[Abstract]:With the development of Internet of things, artificial intelligence, computer science and other related technologies, As an important part of the unmanned vehicle system, environmental awareness plays an important role in the whole vehicle. The environment sensing processing architecture based on dual lidar is designed. Based on this architecture, the paper studies and realizes two difficult problems of environmental perception, which are low lane edge detection in structured environment and negative obstacle detection in unstructured environment. The main research results and innovations in this paper are as follows: 1, aiming at single line in the past. Because of the low point cloud density of multi-line lidar and the poor perceptual ability of special scene, the environment sensing and information fusion processing architecture based on symmetrical installation of dual-line lidar is studied and designed. The experimental results show that the point cloud density of the observation area in front of the vehicle body is greatly increased, and the blind area around the body is reduced. It can solve the problem of environment perception, which is difficult for unmanned vehicle. According to the radar scanning characteristics installed horizontally with double lidar, the distribution characteristics of radar points in obstacle area are analyzed. In this paper, a new algorithm for detection of low edge in structured environment is proposed. The algorithm uses a point cloud segmentation method based on gradient consistency, which can segment radar points quickly. The radar points are divided into ground points and obstacle points efficiently. Then the candidate edge points are extracted from the road surface points and raster maps. Finally, the least square algorithm and the improved RANSAC algorithm are used to extract the edge points. The experimental results show that, The point cloud segmentation algorithm has a good segmentation effect, and the improved RANSAC algorithm has a high real-time performance, which can meet the requirements of unmanned vehicles. A new perception method is proposed for the negative obstacle detection problem in unstructured environment. This method does not depend on the smoothness of the ground, and detects the distribution characteristics of the local point cloud. Firstly, the radar point cloud is mapped to the multi-scale grid, and the point cloud density and the relative height of each grid are counted and marked. The geometric features of negative obstacle are extracted from point cloud data, and the statistical features of grid and geometric features of negative obstacle are correlated to find the key feature points. Finally, the feature points are clustered. The method has been successfully run on an unmanned vehicle. Experiments show that the method has high real-time and reliability. The above research results have been successfully used in the "Xingjian 1" unmanned vehicle, which has participated in the "China Smart vehicle Future Challenge" many times, and has achieved excellent results in the competition.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U463.6;TN958.98

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