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Automatic geometry measurement for curved ramps using inertial measurement unit and 3D LiDAR system

机译:使用惯性测量单元和3D LiDAR系统自动测量弯曲坡道的几何形状

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Although road geometry data can be automatically collected using instruments mounted on survey vehicle, measurement of curved ramp geometry is still of low effectiveness and accuracy due to manual or semi-automatic detection of PC (Point of Curvature)/PT (Point of Tangent) as well as influences of vehicle vibration and wandering. In this study a new method is presented for automatic measurement of ramp geometry in network level using IMU (Inertial Measurement Unit) and 3D LiDAR (Light Detection And Ranging) system. Firstly, horizontal alignment measurements are implemented: 1) an improved K-Mean clustering method and linear fitting method are integrated for automatic PC/PT station detection; 2) an algorithm is developed for automatic lane marking identification and localization for vehicles trajectory calibration; 3) curve radius and length are measured based on roadway centerline. Subsequently, pavement slope is calibrated using IMU and transverse profiling data. Finally, nine segments are chosen from highway ramps as test bed, and validation tests are conducted using the field measurement. The test results show the average errors for curve detection and curve radius measurement are 5.89% and 1.99% respectively, and the P-value for longitudinal and cross slope measurement are 0.621 and 0.989 respectively, which indicate the proposed method is robust in ramp geometry measurement. The significant of the proposed method is three folds. First, it integrates and synchronizes the IMU and 3D LiDAR system in geometry measurement. Second, it solves the common problems of mobile survey on vehicle wandering and vibration. Third, it is of high accuracy and effectiveness, and can be used for roadway survey in network level.
机译:尽管可以使用安装在测量车辆上的仪器来自动收集道路几何数据,但由于手动或半自动检测PC(曲率点)/ PT(切线点)作为弯道几何形状,因此测量效率和准确性仍然较低。以及车辆振动和游荡的影响。在这项研究中,提出了一种使用IMU(惯性测量单元)和3D LiDAR(光检测和测距)系统自动测量网络级坡道几何形状的新方法。首先,进行水平对准测量:1)集成了改进的K-Mean聚类方法和线性拟合方法,用于PC / PT站自动检测; 2)开发了一种算法,用于自动车道标记识别和定位,以进行车辆轨迹校准; 3)曲线半径和长度是根据道路中心线测量的。随后,使用IMU和横向轮廓数据对路面坡度进行校准。最后,从高速公路匝道中选择九个路段作为测试台,并使用现场测量进行验证测试。测试结果表明,曲线检测和曲线半径测量的平均误差分别为5.89%和1.99%,纵向和交​​叉斜率测量的P值分别为0.621和0.989,这表明该方法在斜坡几何测量中是可靠的。所提出的方法的重要意义在于三方面。首先,它在几何测量中集成并同步了IMU和3D LiDAR系统。其次,它解决了移动测量中车辆徘徊和振动的常见问题。第三,它具有较高的准确性和有效性,可用于网络级的巷道勘测。

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