为了提高车道线检测的准确性与鲁棒性,降低光照变化与背景干扰的影响,提出了一种改进的Hough变换耦合密度空间聚类的车道线检测算法。首先,建立车道线模型,将车道边界分解为一系列的小线段,借助最小二乘法来表示车道线中的线段。再利用...为了提高车道线检测的准确性与鲁棒性,降低光照变化与背景干扰的影响,提出了一种改进的Hough变换耦合密度空间聚类的车道线检测算法。首先,建立车道线模型,将车道边界分解为一系列的小线段,借助最小二乘法来表示车道线中的线段。再利用改进的Hough变换对图像中的小线段进行检测。引入具有密度空间聚类方法(density based spatial clustering of applications with noise,DBSCAN),对提取的小线段进行聚类,过滤掉图像中的冗余和噪声,同时保留车道边界的关键信息。随后,利用边缘像素的梯度方向来定义小线段的方向,使得边界同一侧的小线段具有相同的方向,而位于相反车道边界的两个小线段具有相反的方向,通过小线段的方向函数得到车道线段候选簇。最后,根据得到的小线段候选簇,利用消失点来拟合最终车道线。在Caltech数据集与实际道路中进行测试,数据表明:与当前流行的车道线检测算法相比,在光照变化、背景干扰等不良因素下,所以算法呈现出更理想的准确性与稳健,可准确识别正常车道线。展开更多
为提高点云数据的平面拟合精度,降低点云数据中噪点对平面拟合算法的影响,提出一种基于随机采样一致性(random sample consensus,RANSAC)算法的距离加权整体最小二乘(weighted total least squares based distances,WTLSD)平面拟合算法,...为提高点云数据的平面拟合精度,降低点云数据中噪点对平面拟合算法的影响,提出一种基于随机采样一致性(random sample consensus,RANSAC)算法的距离加权整体最小二乘(weighted total least squares based distances,WTLSD)平面拟合算法,即RANSAC-WTLSD平面拟合算法。算法通过RANSAC算法对点云数据进行平面初拟合,基于初拟合平面参数构建初始距离权阵。在经过WTLSD算法对拟合平面参数的迭代计算与距离权阵的反复修正后,求得最终平面拟合参数。通过在仿真点云数据以及实际点云数据中的实验结果表明,该算法比常规平面拟合算法相比,具有更低的单位权中误差,更高的平面拟合精度,具有一定的实际应用价值。该算法适用于小数量级的点云数据平面拟合。展开更多
The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity d...The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.展开更多
Estimation of differential geometric properties on a discrete surface is a fundamental work in computer graphics and computer vision. In this paper, we present an accurate and robust method for estimating differential...Estimation of differential geometric properties on a discrete surface is a fundamental work in computer graphics and computer vision. In this paper, we present an accurate and robust method for estimating differential quantities from unorganized point cloud. The principal curvatures and principal directions at each point are computed with the help of partial derivatives of the unit normal vector at that point, where the normal derivatives are estimated by fitting a linear function to each component of the normal vectors in a neighborhood. This method takes into account the normal information of all neighboring points and computes curvatures directly from the variation of unit normal vectors, which improves the accuracy and robustness of curvature estimation on irregular sampled noisy data. The main advantage of our approach is that the estimation of curvatures at a point does not rely on the accuracy of the normal vector at that point, and the normal vectors can be refined in the process of curvature estimation. Compared with the state of the art methods for estimating curvatures and Darboux frames on both synthetic and real point clouds, the approach is shown to be more accurate and robust for noisy and unorganized point cloud data.展开更多
文摘为了提高车道线检测的准确性与鲁棒性,降低光照变化与背景干扰的影响,提出了一种改进的Hough变换耦合密度空间聚类的车道线检测算法。首先,建立车道线模型,将车道边界分解为一系列的小线段,借助最小二乘法来表示车道线中的线段。再利用改进的Hough变换对图像中的小线段进行检测。引入具有密度空间聚类方法(density based spatial clustering of applications with noise,DBSCAN),对提取的小线段进行聚类,过滤掉图像中的冗余和噪声,同时保留车道边界的关键信息。随后,利用边缘像素的梯度方向来定义小线段的方向,使得边界同一侧的小线段具有相同的方向,而位于相反车道边界的两个小线段具有相反的方向,通过小线段的方向函数得到车道线段候选簇。最后,根据得到的小线段候选簇,利用消失点来拟合最终车道线。在Caltech数据集与实际道路中进行测试,数据表明:与当前流行的车道线检测算法相比,在光照变化、背景干扰等不良因素下,所以算法呈现出更理想的准确性与稳健,可准确识别正常车道线。
文摘为提高点云数据的平面拟合精度,降低点云数据中噪点对平面拟合算法的影响,提出一种基于随机采样一致性(random sample consensus,RANSAC)算法的距离加权整体最小二乘(weighted total least squares based distances,WTLSD)平面拟合算法,即RANSAC-WTLSD平面拟合算法。算法通过RANSAC算法对点云数据进行平面初拟合,基于初拟合平面参数构建初始距离权阵。在经过WTLSD算法对拟合平面参数的迭代计算与距离权阵的反复修正后,求得最终平面拟合参数。通过在仿真点云数据以及实际点云数据中的实验结果表明,该算法比常规平面拟合算法相比,具有更低的单位权中误差,更高的平面拟合精度,具有一定的实际应用价值。该算法适用于小数量级的点云数据平面拟合。
基金funded by the Natural Science Foundation Committee,China(41364001,41371435)
文摘The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.
基金Supported in part by the National Natural Science Foundation of China (Grant Nos. 60672148, 60872120)the National High-Tech Research & Development Program of China (Grant Nos. 2006AA01Z301, 2008AA01Z301)Beijing Municipal Natural Science Foundation (Grant No.4062033)
文摘Estimation of differential geometric properties on a discrete surface is a fundamental work in computer graphics and computer vision. In this paper, we present an accurate and robust method for estimating differential quantities from unorganized point cloud. The principal curvatures and principal directions at each point are computed with the help of partial derivatives of the unit normal vector at that point, where the normal derivatives are estimated by fitting a linear function to each component of the normal vectors in a neighborhood. This method takes into account the normal information of all neighboring points and computes curvatures directly from the variation of unit normal vectors, which improves the accuracy and robustness of curvature estimation on irregular sampled noisy data. The main advantage of our approach is that the estimation of curvatures at a point does not rely on the accuracy of the normal vector at that point, and the normal vectors can be refined in the process of curvature estimation. Compared with the state of the art methods for estimating curvatures and Darboux frames on both synthetic and real point clouds, the approach is shown to be more accurate and robust for noisy and unorganized point cloud data.
