摘要
轨道空间线形检测是保障列车运行安全的一项关键技术,受陀螺仪及加速度计的累计误差的影响,使得基于常规的惯性单元的轨道线形检测方法在低速连续运动测量下精度较低。为了解决该问题,提出一种基于机器视觉与惯性信息多传感器融合的轨道空间线形检测方法。通过分别建立惯性测量单元与机器视觉转换矩阵,倾角仪与惯性测量单元旋转矩阵及惯性测量单元与机器视觉平移关系矩阵,将动态测量数据转换到世界坐标系下,实现多传感器间的融合定标。利用扩展卡尔曼滤波将机器视觉与惯性信息进行融合,提高检测精度。最后,通过搭建测量平台进行实验验证,结果表明该方法的测量精度小于0.5mm且标准差低于0.3。与常规惯性测量方法相比,测量精度提高近10倍。
The track alignment detection is a key technology to ensure the safety of train operation.Due to the cumulative error of the gyroscope and the accelerometer,the track linear detection method based on the conventional inertial measurement has low accuracy under the low-speed.In order to solve this problem,a new detection method for track linear based on multi-sensors fusion of machine vision and inertial measurement is proposed.To achieve multi-sensor fusion calibration,the conversion matrixes of inertial measurement unit and machine vision,inclinometer and inertia measurement unit and translation relationship between inertial measurement unit and machine vision are established,respectively.Machine vision and inertia information fusion is given by extended Kalman filter.Afterwards,the detection performance of theproposed method is investigated by six degrees of freedom platform and experimental line.And the results demonstrate that the measurement accuracy of the new method is less than 0.5 mm.Compared with the conventional inertial measurement method,it is found that the measurement accuracy efficiency of the proposed method has improved nearly 10 times.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2018年第2期394-400,共7页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51405287
51478258)
上海高校青年教师培养资助计划(ZZGCD15118)
上海市科委重点支撑资助项目(18030501300)
上海市科委地方院校能力建设资助项目(15590501400)
关键词
机器视觉
惯性测量
多传感器融合
卡尔曼滤波
轨道空间线形
machine vision
inertial measurement
multi-sensors fusion
Kalman filtering algorithm
track alignment
作者简介
郑树彬,男,1979年8月生,博士、副教授、硕士生导师.主要研究方向为轨道状态检测.曾发表《基于动态模板的钢轨磨耗测量方法研究》（《中国铁道科学》2013年第34卷第2期）等论文.E-mail：zhengshubin@126.com
