基于路面3D激光点云数据的车辙自动测量与横向定位
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摘要
针对传统车辙检测方法存在的受车道外杂物、拥包和推波影响,且无法横向定位车辙于车道内位置的问题,提出一种结合路面水平轴和车道边缘线坐标的车辙检测定位方法。研究以2D/3D激光点云数据为研究基础,首先通过改进Canny算法对2D数据中的车道边缘线进行识别定位,从而提取车道内区域数据以消除车道外杂物干扰;然后参照构建的路面水平轴,进行车辙端点和谷底点的搜寻定位;最后结合车辙特征点连续精准测量车辙宽度、最大深度,以及于车道内的横向位置。在实地采集的大量数据基础上,通过与人工测量结果对比得出,该算法具有较高的准确性,其测量的车辙最大深度和宽度与人工检测结果平均相对误差分别为1%和2%,车辙横向定位与人工检测结果相关度高于90%。
The traditional rutting detection method is affected by the debris outside the lane,swells and translation.It is unable to locate the lateral position of the rutting. The improved method proposed in this study combines the horizontal axis of the road and the edge of the lane marking for rutting detection. Based on the 2 D/3 D laser point cloud data,the improved Canny algorithm is applied to locate the lane edge line using 2 D data. The edge points and the valley point of rutting are detected by referencing the constructed road axis. Finally,the width,the maximum depth,and the lateral position of rutting are continuously measured. By comparing with manual measurement,the validation test shows that the proposed method has high accuracy in rutting measurement( the average error of the maximum depth and width measurement are 1% and 2% respectively,and the correlation coefficient for rutting positioning is higher than 90%).
The traditional rutting detection method is affected by the debris outside the lane,swells and translation.It is unable to locate the lateral position of the rutting. The improved method proposed in this study combines the horizontal axis of the road and the edge of the lane marking for rutting detection. Based on the 2 D/3 D laser point cloud data,the improved Canny algorithm is applied to locate the lane edge line using 2 D data. The edge points and the valley point of rutting are detected by referencing the constructed road axis. Finally,the width,the maximum depth,and the lateral position of rutting are continuously measured. By comparing with manual measurement,the validation test shows that the proposed method has high accuracy in rutting measurement( the average error of the maximum depth and width measurement are 1% and 2% respectively,and the correlation coefficient for rutting positioning is higher than 90%).
引文
[1] Fwa T,Pasindu H,Ong G.Critical Rut Depth for Pavement Maintenance Based on Vehicle Skidding and Hydroplaning Consideration[J].Transportation Engineering,2012,138(4):423-429.
[2] 张德津,李清泉,何莉.一种新的激光深度测量方法研究[J].光学学报,2013,33(1):115-121.
[3] 啜二勇.国外路面自动检测系统发展综述[J].交通运输研究,2009(17):96-99.
[4] 毛志坚,金松涛,朱栋.浅析国内外路面车辙检测新技术[J].公路交通科技,2009,26(5):408-411.
[5] AASHTO Provisional Standards PP 38-00:Standard Practice for Determining Maximum Rut Depth in Asphalt Pavements[S].American Association of State Highway and Transportation Officials,Washington,D.C.,USA,2001.
[6] Louw K.Modeling Rutting in Flexible Pavements in HDM-4[R].The Highway Development and Management Series,1995.
[7] Cenek P D,Patrick J E,McGuire J F,et al.New Zealand Experience in Comparing Manual and Automatic Pavement Condition Rating Systems[C]//Third International Conference on Managing Pavements,1994,Transportation Research Board.
[8] 洪梓铭,陈昆,荆根强,等.基于线激光的自然条件下路面车辙实时检测方法研究[J].红外与激光工程,2018,47(6):617007-0617007(8).
[9] Luo Wenting,Wang K C P,Li Lin.Surface Drainage Evaluation for Rigid Pavements Using IMU and 1 mm 3D Texture Data[J].Transportation Research Record:Journal of the Transportation Research Board,Pavement Management,2014,3:121-128.
[10] Luo Wenting,WANG K C P,Li Lin.Hydroplaning on Sloping Pavements Based on Inertial Measurement Unit(IMU)and 1 mm 3D Laser Imaging Data[J].Periodica Polytechnica Transportation Engineering,2016,44(1):42-49.
[11] Luo Wenting,Li Lin,Automatic Geometry Measurement for Curved Ramps Using Inertial Measurement Unit and 3D LiDAR System[J].Automation in Construction,2018,94,214-232.
[12] Li Lin,Luo Wenting,Wang K C P,et al.Automatic Groove Measurement and Evaluation with High Resolution Laser Profiling Data[J].Sensors,2018,18(8):2713.
[13] 罗文婷,李中轶,李林等.基于改进Canny边缘检测算法的道路标线自动识别及定位[J].西南交通大学学报,2018,53(6):1253-1260.
[14] Li Lin,Luo Wenting,Wang K C P,Lane Marking Detection and Reconstruction with Line-Scan Imaging Data[J].Sensors,2018,18(5):1635.
[15] Simpson A L.Measurement of a Rut[C/CD].Transportation Research Board 82nd Annual Meeting,Washington,D.C.,USA,2003.
[16] 罗楠欣.基于路面高精三维图像的车辙识别与评价[D].成都:西南交通大学,2015.
[17] 李甜甜.基于三维线激光技术的路面车辙检测技术研究[D].西安:长安大学,2016.
[18] 孙益刚,张春乐.基于卡尔曼滤波的机场道面接缝积水检测方法研究[J].传感技术学报,2017,30(8):1204-1208.
[19] 和松,李福普,常成利,等.公路路基路面现场测试规程(JTG E60-2008)[M].北京:交通部公路科学研究院,2008.
[20] 陈晓飞,凌有铸,陈孟元.WSNs环境下基于高斯混合容积卡尔曼滤波的移动机器人定位算法[J].传感技术学报,2017,30(1):133-138.
[2] 张德津,李清泉,何莉.一种新的激光深度测量方法研究[J].光学学报,2013,33(1):115-121.
[3] 啜二勇.国外路面自动检测系统发展综述[J].交通运输研究,2009(17):96-99.
[4] 毛志坚,金松涛,朱栋.浅析国内外路面车辙检测新技术[J].公路交通科技,2009,26(5):408-411.
[5] AASHTO Provisional Standards PP 38-00:Standard Practice for Determining Maximum Rut Depth in Asphalt Pavements[S].American Association of State Highway and Transportation Officials,Washington,D.C.,USA,2001.
[6] Louw K.Modeling Rutting in Flexible Pavements in HDM-4[R].The Highway Development and Management Series,1995.
[7] Cenek P D,Patrick J E,McGuire J F,et al.New Zealand Experience in Comparing Manual and Automatic Pavement Condition Rating Systems[C]//Third International Conference on Managing Pavements,1994,Transportation Research Board.
[8] 洪梓铭,陈昆,荆根强,等.基于线激光的自然条件下路面车辙实时检测方法研究[J].红外与激光工程,2018,47(6):617007-0617007(8).
[9] Luo Wenting,Wang K C P,Li Lin.Surface Drainage Evaluation for Rigid Pavements Using IMU and 1 mm 3D Texture Data[J].Transportation Research Record:Journal of the Transportation Research Board,Pavement Management,2014,3:121-128.
[10] Luo Wenting,WANG K C P,Li Lin.Hydroplaning on Sloping Pavements Based on Inertial Measurement Unit(IMU)and 1 mm 3D Laser Imaging Data[J].Periodica Polytechnica Transportation Engineering,2016,44(1):42-49.
[11] Luo Wenting,Li Lin,Automatic Geometry Measurement for Curved Ramps Using Inertial Measurement Unit and 3D LiDAR System[J].Automation in Construction,2018,94,214-232.
[12] Li Lin,Luo Wenting,Wang K C P,et al.Automatic Groove Measurement and Evaluation with High Resolution Laser Profiling Data[J].Sensors,2018,18(8):2713.
[13] 罗文婷,李中轶,李林等.基于改进Canny边缘检测算法的道路标线自动识别及定位[J].西南交通大学学报,2018,53(6):1253-1260.
[14] Li Lin,Luo Wenting,Wang K C P,Lane Marking Detection and Reconstruction with Line-Scan Imaging Data[J].Sensors,2018,18(5):1635.
[15] Simpson A L.Measurement of a Rut[C/CD].Transportation Research Board 82nd Annual Meeting,Washington,D.C.,USA,2003.
[16] 罗楠欣.基于路面高精三维图像的车辙识别与评价[D].成都:西南交通大学,2015.
[17] 李甜甜.基于三维线激光技术的路面车辙检测技术研究[D].西安:长安大学,2016.
[18] 孙益刚,张春乐.基于卡尔曼滤波的机场道面接缝积水检测方法研究[J].传感技术学报,2017,30(8):1204-1208.
[19] 和松,李福普,常成利,等.公路路基路面现场测试规程(JTG E60-2008)[M].北京:交通部公路科学研究院,2008.
[20] 陈晓飞,凌有铸,陈孟元.WSNs环境下基于高斯混合容积卡尔曼滤波的移动机器人定位算法[J].传感技术学报,2017,30(1):133-138.