【摘要】：無人駕駛中的環(huán)境感知系統(tǒng)是實現(xiàn)無人駕駛的重要部分,也是無人駕駛車輛安全行駛的第一環(huán),而可通行區(qū)域則是環(huán)境感知系統(tǒng)中的基礎(chǔ)工作�；赩elodyne HDL-32E激光雷達(dá),本文提出了基于多幀激光點云數(shù)據(jù)的可通行區(qū)域提取方法和框架。具體研究內(nèi)容包含以下幾個方面:1)在介紹Velodyne HDL-32E激光雷達(dá)特性和數(shù)據(jù)結(jié)構(gòu)的基礎(chǔ)上,提出了在多幀點云數(shù)據(jù)中進(jìn)行可通行區(qū)域提取的框架:首先在單幀激光點云數(shù)據(jù)中提取可通行區(qū)域,然后基于POS數(shù)據(jù)或者SLAM算法對連續(xù)多幀數(shù)據(jù)進(jìn)行對準(zhǔn),最后以單幀數(shù)據(jù)中的提取結(jié)果為基礎(chǔ),在對準(zhǔn)的點云數(shù)據(jù)中再次進(jìn)行可通行區(qū)域提取,得到最終結(jié)果。2)研究單幀點云數(shù)據(jù)中可通行區(qū)域的提取方法。提出基于豎直平面上連續(xù)點夾角的可通行區(qū)域提取方法,確定了對于不同激光掃描線角度閾值的計算方法。3)對比研究了基于POS和基于SLAM算法的連續(xù)多幀點云數(shù)據(jù)對準(zhǔn)方法。針對可通行區(qū)域提取這一激光點云應(yīng)用實例,提出了基于主成份分析的點云對準(zhǔn)效果評價方法。4)研究了在對準(zhǔn)后多幀激光雷達(dá)點云中實時提取可通行區(qū)域的方法。首先基于體素(Voxel)的方法對障礙物點云中的重復(fù)、鄰近點進(jìn)行了去除。然后基于八叉樹在對準(zhǔn)后的原始點云中建立空間索引。最后通過鄰近點的高度差對障礙物點進(jìn)行驗證,剔除障礙物點云中存在的誤檢點。通過使用多個真實場景采集的數(shù)據(jù)的實驗驗證,證明了本文可通行區(qū)域提取方法可同時運用于城市環(huán)境和野外環(huán)境。
[Abstract]:The environment sensing system in driverless is an important part of realizing unmanned driving and the first link of driving safety of driverless vehicle. The passable area is the basic work in the environment sensing system. Based on Velodyne HDL-32E lidar, this paper presents a method and framework for extracting passable region based on multi-frame laser point cloud data. The specific research contents include the following aspects: 1) on the basis of introducing the characteristics and data structure of Velodyne HDL-32E lidar, A framework for extracting passable regions from multi-frame point cloud data is proposed. Firstly, the passable region is extracted from single-frame laser point cloud data, and then the continuous multi-frame data is aligned based on POS data or SLAM algorithm. Finally, based on the extraction results of single frame data, the passable region is extracted again in the aligned point cloud data, and the final results are obtained. 2) the method of extracting passable area from single frame point cloud data is studied. In this paper, a method of extracting passable region based on the angle of continuous points on vertical plane is proposed, and the calculation method of angle threshold of different laser scanning lines is determined. 3) the method of continuous multi-frame point cloud data alignment based on POS and SLAM algorithm is compared and studied. Aiming at the application example of laser point cloud extraction from passable area, a point cloud alignment evaluation method based on principal component analysis (PCA) is proposed. 4) the method of extracting passable area in multi-frame lidar point cloud after alignment is studied in real time. Firstly, the repeat and adjacent points in obstacle point cloud are removed based on voxel (Voxel) method. Then the spatial index is built based on octree in the original point cloud after alignment. Finally, the obstacle points are verified by the height difference of the adjacent points, and the false detection points in the obstacle point cloud are eliminated. It is proved that this method can be used in both urban environment and field environment through the experimental verification of data collected from many real scenes.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN958.98

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