一种新的轨距动态检测方法研究
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摘要
提出一种基于激光摄像检测的轨距计算新算法,该算法将按照轨距定义寻找轨顶踏面下16 mm内2股钢轨工作边之间的最小距离作为轨距测量值。考虑摄像机镜头畸变影响,建立2个激光摄像式传感器之间空间姿态关系的非线性标定模型,并给出一种标定方案。为了能快速定位轮廓特征点,提出钢轨轮廓特征曲线的概念,并给出特征曲线的一种定义方式,利用特征曲线能快速寻找轮廓特征点。与传统检测方法相比,新算法只用了2个激光摄像式传感器,不需要其他辅助传感器来对车体振动进行修正。最后通过实际地铁线路设置障碍试验,验证了该检测方法具有一定的抗干扰能力、稳定性高、结构简单、在线计算实时性强和检测精度高等特点。
A new algorithm of gauge calculation based on laser camera detection was proposed. This algorithm, according to track gauge defined, finds the minimum distance in 16 mm between two rails work side under rail top tread as track gauge measuring value. Considering the influence of camera lens distortion, a nonlinear calibration model of spatial attitude relationship between two laser camera sensors was established, and a calibration program was proposed. In order to quickly locate the profile feature points, this paper put forward the concept of the rail profile feature curve, and proposed a definition of the characteristic curve. Using feature curve can rapidly find the profile feature points. Compared with the traditional detection method, the new algorithm only uses two laser camera sensors, and does not need other auxiliary sensors to modify the car body vibration. Finally, by setting obstacle test through the actual subway line, it is approved that this detection method has the characteristics of certain anti-interference ability, high stability, simple structure, strong real-time online calculation, high detection accuracy and so on.
A new algorithm of gauge calculation based on laser camera detection was proposed. This algorithm, according to track gauge defined, finds the minimum distance in 16 mm between two rails work side under rail top tread as track gauge measuring value. Considering the influence of camera lens distortion, a nonlinear calibration model of spatial attitude relationship between two laser camera sensors was established, and a calibration program was proposed. In order to quickly locate the profile feature points, this paper put forward the concept of the rail profile feature curve, and proposed a definition of the characteristic curve. Using feature curve can rapidly find the profile feature points. Compared with the traditional detection method, the new algorithm only uses two laser camera sensors, and does not need other auxiliary sensors to modify the car body vibration. Finally, by setting obstacle test through the actual subway line, it is approved that this detection method has the characteristics of certain anti-interference ability, high stability, simple structure, strong real-time online calculation, high detection accuracy and so on.
引文
[1]中华人民共和国铁道部.铁路线路维修规则[S].Ministry of Railways of China.Railway track maintenance standards[S].
[2]陈东生,田新宇.中国高速铁路轨道检测技术发展[J].铁道建筑,2008(12):82-86.CHEN Dongsheng,TIAN Xinyu.The development of China’s high-speed railway inspection technology[J].Railway Engineering,2008(12):82-86.
[3]史红梅,张继科.基于激光三角测量原理的轨距检测系统研究[J].仪器仪表学报,2013,34(9):1934-1940.SHI Hongmei,ZHANG Jike.Study on track gauge measurement system based on laser triangulation principle[J].Chinese Journal of Scientific Instrument,2013,34(9):1934-1940.
[4]郑树彬,柴晓冬,韩国阁,等.基于机器视觉的轨距检测方法研究[J].城市軌道交通研究,2010,13(9):73-76.ZHENG Shubin,CHAI Xiaodong,HAN Guoge,et al.Gauge detection method based on machine vision[J].Urban Mass Transit,2010,13(9):73-76.
[5]闵永智,王红霞,康飞,等.基于图像式传感器的铁路轨距检测系统研究[J].激光技术,2015,39(3):344-348.MIN Yongzhi,WANG Hongxia,KANG Fei,et al.Study on rail gauge detection system based on image sensors[J].Laser Technology,2015,39(3):344-348.
[6]YANG Q,LIN J.Track gauge dynamic measurement based on 2D laser displacement sensor[C]//Mechanic Automation and Control Engineering(MACE),2011Second International Conference on IEEE,2011:5473-5476.
[7]ZHANG Z.A flexible new technique for camera calibration[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[8]占栋,于龙,肖建,等.轨道检测中激光摄像式传感器标定方法研究[J].机械工程学报,2013,49(16):39-47.ZHAN Dong,YU Long,XIAO Jian,et al.Calibration approach study for the laser camera transducer of track inspection[J].Journal of Mechanical Engineering,2103,49(16):39-47.
[9]Popov D V,Ryabichenko R B,Krivosheina E A.Calibration of the CCD photonic measuring system for railway inspection[C]//International Society for Optics and Photonics,2005:592015-592015-9.
[10]Weng J,Cohen P,Herniou M.Camera calibration with distortion models and accuracy evaluation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(10):965-980.
[11]Ricolfe-Viala C,Sánchez-Salmerón A J.Robust metric calibration of non-linear camera lens distortion[J].Pattern Recognition,2010,43(4):1688-1699.
[12]WANG J,SHI F,ZHANG J,et al.A new calibration model of camera lens distortion[J].Pattern Recognition,2008,41(2):607-615.
[13]张文景,许晓鸣.一种基于曲率提取轮廓特征点的方法[J].上海交通大学学报,1999,33(5):592-595.ZHANG Wenjing,XU Xiaoming.Approach to extract feature points on boundary based on curvature[J].Journal of Shanghai Jiaotong University,1999,33(5):592-595.
[14]Alippi C,Casagrande E,Scotti F,et al.Composite real-time image processing for railways track profile measurement[J].IEEE Transactions on Instrumentation and Measurement,2000,49(3):559-564.
[15]Menaka R,Chellamuthu C,Karthik R.Efficient feature point detection in CT images using discrete curvelet transform[J].Journal of Scientific&Industrial Research,2013,72(5):312-315.
[16]REN S,GU S,XU G,et al.A new track inspection car based on a laser camera system[J].Chinese Optics Letters,2011,9(3):031202
[2]陈东生,田新宇.中国高速铁路轨道检测技术发展[J].铁道建筑,2008(12):82-86.CHEN Dongsheng,TIAN Xinyu.The development of China’s high-speed railway inspection technology[J].Railway Engineering,2008(12):82-86.
[3]史红梅,张继科.基于激光三角测量原理的轨距检测系统研究[J].仪器仪表学报,2013,34(9):1934-1940.SHI Hongmei,ZHANG Jike.Study on track gauge measurement system based on laser triangulation principle[J].Chinese Journal of Scientific Instrument,2013,34(9):1934-1940.
[4]郑树彬,柴晓冬,韩国阁,等.基于机器视觉的轨距检测方法研究[J].城市軌道交通研究,2010,13(9):73-76.ZHENG Shubin,CHAI Xiaodong,HAN Guoge,et al.Gauge detection method based on machine vision[J].Urban Mass Transit,2010,13(9):73-76.
[5]闵永智,王红霞,康飞,等.基于图像式传感器的铁路轨距检测系统研究[J].激光技术,2015,39(3):344-348.MIN Yongzhi,WANG Hongxia,KANG Fei,et al.Study on rail gauge detection system based on image sensors[J].Laser Technology,2015,39(3):344-348.
[6]YANG Q,LIN J.Track gauge dynamic measurement based on 2D laser displacement sensor[C]//Mechanic Automation and Control Engineering(MACE),2011Second International Conference on IEEE,2011:5473-5476.
[7]ZHANG Z.A flexible new technique for camera calibration[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[8]占栋,于龙,肖建,等.轨道检测中激光摄像式传感器标定方法研究[J].机械工程学报,2013,49(16):39-47.ZHAN Dong,YU Long,XIAO Jian,et al.Calibration approach study for the laser camera transducer of track inspection[J].Journal of Mechanical Engineering,2103,49(16):39-47.
[9]Popov D V,Ryabichenko R B,Krivosheina E A.Calibration of the CCD photonic measuring system for railway inspection[C]//International Society for Optics and Photonics,2005:592015-592015-9.
[10]Weng J,Cohen P,Herniou M.Camera calibration with distortion models and accuracy evaluation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(10):965-980.
[11]Ricolfe-Viala C,Sánchez-Salmerón A J.Robust metric calibration of non-linear camera lens distortion[J].Pattern Recognition,2010,43(4):1688-1699.
[12]WANG J,SHI F,ZHANG J,et al.A new calibration model of camera lens distortion[J].Pattern Recognition,2008,41(2):607-615.
[13]张文景,许晓鸣.一种基于曲率提取轮廓特征点的方法[J].上海交通大学学报,1999,33(5):592-595.ZHANG Wenjing,XU Xiaoming.Approach to extract feature points on boundary based on curvature[J].Journal of Shanghai Jiaotong University,1999,33(5):592-595.
[14]Alippi C,Casagrande E,Scotti F,et al.Composite real-time image processing for railways track profile measurement[J].IEEE Transactions on Instrumentation and Measurement,2000,49(3):559-564.
[15]Menaka R,Chellamuthu C,Karthik R.Efficient feature point detection in CT images using discrete curvelet transform[J].Journal of Scientific&Industrial Research,2013,72(5):312-315.
[16]REN S,GU S,XU G,et al.A new track inspection car based on a laser camera system[J].Chinese Optics Letters,2011,9(3):031202