UKF-based three-dimensional track generation method for digital track map
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摘要: 针对基于卫星导航系统的列车定位对数字轨道地图的实际需求, 提出了一种基于无迹卡尔曼滤波的线路估计方法, 生成线路的三维数字轨道地图; 对于铁路线路的3种平面线形(直线、缓和曲线和圆曲线), 采用以里程为参数的菲涅尔(Fresnel)积分模型统一建模; 对于纵断面的直线和曲线, 采用二次曲线模型建模; 用无迹卡尔曼滤波对模型的状态(里程、三维坐标)和参数(方位角、曲率、曲率变化率、坡度、坡度变化率)进行联合估计; 将归一化新息平方和估计距离误差作为线路分段的判断条件, 最终用分段点和几何参数完成三维线路的生成; 采用仿真的平面线路数据对比了离散点法、三次多项式法和本文Fresnel法, 利用青藏线14.7 km的实测数据进一步对Fresnel法进行了验证。仿真结果表明: 在相同的误差要求下, 3种方法的平面距离误差均值都在0.024 m以内, 但Fresnel法采用了最少的分段点, 数据约简率高达99.76%; Fresnel法的最大累积里程误差最小, 由0.964 m降低为0.060 m, 减少了93.77%;Fresnel法比三次多项式法的方位角和曲率估计精度都高, 更加接近真值; 实际数据测试结果表明Fresnel法分别采用22个和20个分段点及参数即可完成线路的平面曲线和纵断面曲线生成, 平面和纵断面曲线距离误差均值都在0.03 m以内, 累积里程误差最大只有0.078 m, 位置精度和几何精度都较高。Abstract: To meet the requirements of digital track maps for the satellite-navigation-system-based train positioning, an unscented Kalman filter(UKF)-based track estimation method was proposed and a three-dimensional digital track map for railway tracks was generated. For the three basic curve elements(straight line, transition curve, and circular arc) in the horizontal profile of railway track, a mileage-parameterized Fresnel integral model was used for a unified modeling. For the straight line and curve in the vertical profile, a quadratic curve model was used for modeling. The states(mileage, three-dimensional coordinates) and parameters(heading, curvature, curvature rate, slope, and slope rate) of models were jointly estimated using the UKF. The normalized innovation squared and estimated distance error were introduced as the criteria to segment the track. The three-dimensional railway track was generated using the breakpoints along with the geometric parameters. The discrete point, cubic polynomial, and proposed Fresnel integral methods were compared by using the simulated horizontal track data. The Fresnel method was verified by using the 14.7 km field data from the Qinghai-Tibet Railway Line. Simulation result shows that the mean horizontal distance errors are below 0.024 m for all three methods under the same error requirement. However, the Fresnel method uses the fewest break points, with a data reduction rate of 99.76%. In addition, the maximum chainage error of Fresnel method is the smallest, which decreases from 0.964 m to only 0.060 m, with a reduction of 93.77%. The heading and curvature of Fresnel method are considerably more accurate than those of the cubic polynomial method, which are closer to the true value. The field data test results demonstrate that the Fresnel method can use 22 and 20 break points with their parameters to generate the horizontal and vertical curves, respectively. The mean distance errors of horizontal and vertical curves are below 0.03 m, while the maximum accumulative mileage error is only 0.078 m, which indicates high accuracies of both position and geometry.
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表 1 仿真结果误差对比
Table 1. Error comparison of simulation results
误差类别 方法 最大值 平均值 标准差 平面距离/m DP算法 0.079 0.024 0.018 三次多项式法 0.079 0.019 0.012 Fresnel法 0.077 0.019 0.013 累积里程/m DP算法 0.091 0.015 0.022 三次多项式法 0.964 0.467 0.269 Fresnel法 0.060 -0.008 0.021 方位角/rad 三次多项式法 4.759×10-3 2.964×10-6 7.002×10-4 Fresnel法 1.316×10-4 2.956×10-6 1.703×10-5 曲率/(rad·m-1) 三次多项式法 2.415×10-4 1.232×10-6 4.258×10-5 Fresnel法 3.046×10-5 7.253×10-8 1.117×10-6 
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表 2 实际线路数据平纵面误差结果
Table 2. Horizontal and vertical error results of field track
类别 约简率/% 距离误差最大值/m 距离误差均值/m 距离误差标准差/m 平面曲线 99.64 0.100 0.030 0.024 纵断面曲线 99.67 0.100 0.024 0.021
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[1] OTEGUI J, BAHILLO A, LOPETEGI I, et al. A survey of train positioning solutions[J]. IEEE Sensors Journal, 2017, 17(20): 6788-6797. doi: 10.1109/JSEN.2017.2747137 [2] 刘江, 蔡伯根, 王剑. 基于卫星导航系统的列车定位技术现状与发展[J]. 中南大学学报(自然科学版), 2014, 45(11): 4033-4042. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201411044.htmLIU Jiang, CAI Bai-gen, WANG Jian. Status and development of satellite navigation system based train positioning technology[J]. Journal of Central South University (Science and Technology), 2014, 45(11): 4033-4042. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201411044.htm [3] 上官伟, 袁重阳, 蔡伯根, 等. 北斗二代在西部低密度铁路中的应用[J]. 交通运输工程学报, 2016, 16(5): 132-141. doi: 10.3969/j.issn.1671-1637.2016.05.015SHANGGUAN Wei, YUAN Chong-yang, CAI Bai-gen, et al. Application of BDS in western low-density railway lines[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 132-141. (in Chinese). doi: 10.3969/j.issn.1671-1637.2016.05.015 [4] 郭子明, 蔡伯根, 姜维, 等. 基于贝叶斯建模的轨道占用识别方法[J]. 交通运输系统工程与信息, 2020, 20(1): 47-53. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202001009.htmGUO Zi-ming, CAI Bai-gen, JIANG Wei, et al. A track occupancy identification approach based on Bayesian modeling[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 47-53. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202001009.htm [5] JIANG Qing-an, WU Wen-qi, JIANG Ming-ming, et al. A new filtering and smoothing algorithm for railway track surveying based on landmark and IMU/Odometer[J]. Sensors, 2017, 17(6): 1-20. doi: 10.1109/JSEN.2017.2656005 [6] 李清泉, 毛庆洲. 道路/轨道动态精密测量进展[J]. 测绘学报, 2017, 46(10): 1734-1741. doi: 10.11947/j.AGCS.2017.20170323LI Qing-quan, MAO Qing-zhou. Progress on dynamic and precise engineering surveying for pavement and track[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1734-1741. (in Chinese). doi: 10.11947/j.AGCS.2017.20170323 [7] 李广云, 范百兴. 精密工程测量技术及其发展[J]. 测绘学报, 2017, 46(10): 1742-1751. doi: 10.11947/j.AGCS.2017.20170313LI Guang-yun, FAN Bai-xing. The development of precise engineering surveying technology[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1742-1751. (in Chinese). doi: 10.11947/j.AGCS.2017.20170313 [8] 吴小宁, 蒲文奎. 青藏铁路ITCS系统专用地图数据测绘优化方案[J]. 铁道通信信号, 2019, 55(2): 63-66. https://www.cnki.com.cn/Article/CJFDTOTAL-TDTH201902018.htmWU Xiao-ning, PU Wen-kui. Optimized track surveying scheme of digital map for ITCS system in Qinghai-Tibet Railway[J]. Railway Signalling and Communication, 2019, 55(2): 63-66. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TDTH201902018.htm [9] LIU Jiang, CAI Bai-gen, WANG Jian. Electronic track map building for satellite-based high integrity railway train positioning[J]. International Journal on Smart Sensing and Intelligent Systems, 2013, 6(2): 610-629. doi: 10.21307/ijssis-2017-557 [10] 曾强, 陈德旺, 王丽娟, 等. 基于主曲线和自适应半径的多GPS轨迹数据融合算法[J]. 铁道学报, 2015, 37(2): 46-51. doi: 10.3969/j.issn.1001-8360.2015.02.007ZENG Qiang, CHEN De-wang, Wang Li-juan, et al. Multiple GPS data fusion algorithm based on principal curves and adaptive radius method[J]. Journal of the China Railway Society, 2015, 37(2): 46-51. (in Chinese). doi: 10.3969/j.issn.1001-8360.2015.02.007 [11] HEIRICH O, ROBERTSON P, STRANG T. RailSLAM—localization of rail vehicles and mapping of geometric railway tracks[C]//IEEE. Proceedings of IEEE International Conference on Robotics and Automation (ICRA). New York: IEEE, 2013: 5212-5219. [12] HEIRICH O. Bayesian train localization with particle filter, loosely coupled GNSS, IMU, and a track map[J]. Journal of Sensors, 2016, DOI: 10.1155/2016/2672640. [13] 陶璐, 朱敦尧, 王军德, 等. 一种基于里程参数的道路平面几何解析模型[J]. 测绘通报, 2017(3): 52-57. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB201703013.htmTAO Lu, ZHU Dun-yao, WANG Jun-de, et al. A road plane geometry analytical model based on mileage parameter[J]. Bulletin of Surveying and Mapping, 2017(3): 52-57. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB201703013.htm [14] JO K, SUNWOO M. Generation of aprecise roadway map for autonomous cars[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15 (3): 925-937. doi: 10.1109/TITS.2013.2291395 [15] JO K, LEE M, KIM J, et al. Tracking and behavior reasoning of moving vehicles based on roadway geometry constraints[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(2): 460-476. [16] GWON G P, HUR W S, KIM S W, et al. Generation of a precise and efficient lane-level road map for intelligent vehicle systems[J]. IEEE Transactions on Vehicular Technology, 2017, 66(6): 4517-4533. doi: 10.1109/TVT.2016.2535210 [17] GARACH L, DE OÑA J, PASADAS M. Mathematical formulation and preliminary testing of a spline approximation algorithm for the extraction of road alignments[J]. Automation in Construction, 2014, 47: 1-9. doi: 10.1016/j.autcon.2014.07.002 [18] CAMACHO-TORREGROSA F J, PÉREZ-ZURIAGA A M, CAMPOY-UNGRÍA J M, et al. Use of heading direction for recreating the horizontal alignment of an existing road[J]. Computer-Aided Civil and Infrastructure Engineering, 2015, 30(4): 282-299. doi: 10.1111/mice.12094 [19] HOLGADO-BARCO A, GONZÁLEZ-AGUILERA D, ARIAS-SANCHEZ P, et al. Semiautomatic extraction of road horizontal alignment from a mobile LiDAR system[J]. Computer-Aided Civil and Infrastructure Engineering, 2014, 30(3): 217-228. [20] GIKAS V, STRATAKOS J. A novel geodetic engineering method for accurate and automated road/railway centerline geometry extraction based on the bearing diagram and fractal behavior[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(1): 115-126. doi: 10.1109/TITS.2011.2163186 [21] BETAILLE D, TOLEDO-MOREO R. Creating enhanced maps for lane-level vehicle navigation[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(4): 786-798. doi: 10.1109/TITS.2010.2050689 [22] 郝雨时, 徐爱功, 章红平, 等. 车载POS公路线形特征识别与参数计算[J]. 武汉大学学报·信息科学版, 2018, 43(8): 1249-1255. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201808018.htmHAO Yu-shi, XU Ai-gong, ZHANG Hong-ping, et al. Road recognition and calculation of relevant parameters with POS[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1249-1255. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201808018.htm [23] LI Wei, PU Hao, SCHONFELD P, et al. A method for automatically recreating the horizontal alignment geometry of existing railways[J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(1): 71-94. doi: 10.1111/mice.12392 [24] 李伟, 周雨, 王杰, 等. 基于点线一致的既有铁路线路纵断面自动重构方法[J]. 铁道科学与工程学报, 2019, 16(11): 2684-2691. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201911006.htmLI Wei, ZHOU Yu, WANG Jie, et al. Automatic recreating vertical alignment of existing railway based on points-alignment consistency[J]. Journal of Railway Science and Engineering, 2019, 16(11): 2684-2691. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201911006.htm [25] WINTER H, WILLERT V, ADAMY J. Increasing accuracy in train localization exploiting track-geometry constraints[C]//IEEE. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). New York: IEEE, 2018: 1572-1579. [26] WINTER H, LUTHARDT S, WILLERT V, et al. Generating compact geometric track-maps for train positioning applications[C]∥IEEE. 2019 IEEE Intelligent Vehicles Symposium (IV). New York: IEEE, 2019: 1027-1032. [27] DEFRUTOS S H, CASTRO M. A method to identify and classify the vertical alignment of existing roads[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32: 952-963. doi: 10.1111/mice.12302 [28] 薛新功, 李伟, 蒲浩. 铁路线路智能优化方法研究综述[J]. 铁道学报, 2018, 40(3): 6-15. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201803003.htmXUE Xin-gong, LI Wei, PU Hao. Review on intelligent optimization methods for railway alignment[J]. Journal of the China Railway Society, 2018, 40(3): 6-15. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201803003.htm [29] JULIER S J, UHLMANN J K, DURRANT-WHYTE H F. A new approach for filtering nonlinear systems[C]//IEEE. Proceedings of 1995 American Control Conference. New York: IEEE, 1995: 1628-1632. [30] JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401-422. [31] 孙作雷, 李影, 张波, 等. 基于一致性校验的贝叶斯估计器性能评估[J]. 系统仿真学报, 2016, 28(3): 569-576. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201603009.htmSUN Zuo-lei, LI Ying, ZHANG Bo, et al. Performance evaluation of Bayesian estimator with consistency validation[J]. Journal of System Simulation, 2016, 28(3): 569-576. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201603009.htm -
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