﻿ 基于惯导信息的地图匹配算法
«上一篇
 文章快速检索 高级检索

 哈尔滨工程大学学报  2017, Vol. 38 Issue (8): 1268-1272  DOI: 10.11990/jheu.201610055 0

### 引用本文

DENG Zhihong, SUN Liang, FU Mengyin, et al. New map-matching algorithm based on inertial navigation system[J]. Journal of Harbin Engineering University, 2017, 38(8), 1268-1272. DOI: 10.11990/jheu.201610055.

### 文章历史

1. 北京理工大学 自动化学院, 北京 100081;
2. 南京理工大学 自动控制系, 南京 210094

New map-matching algorithm based on inertial navigation system
DENG Zhihong1, SUN Liang1, FU Mengyin1,2, WANG Bo1
1. School of Automation, Beijing Institute of Technology, Beijing 100081, China;
2. Department of Automatic control, Nanjing University of Science and Technology, Nanjing 210094, China
Abstract: To suppress the divergence of navigation error, a new map-matching algorithm was presented based on the position information of an inertial navigation system (INS). The algorithm proposed the concept of translation vector according to the characteristics of the information of INS. An improved projection method was presented to correct the translation vector. Experimental results of a car moving in real time show that the map-matching algorithm based on INS maximizes the information of INS and is more efficient and precise than the traditional map-matching algorithm. Thus, our findings serve as a foundation for error correction of INS based on map-matching information.
Key words: inertial navigation system (INS)    map matching algorithm    translation vector    error of longitudinal position    error of lateral position    error correction

1 惯导/地图匹配算法 1.1 基于平移向量的道路搜索方法

INS的误差主要包括三种形式的周期振荡：舒拉周期振荡、地球周期振荡和付科周期振荡。对于装载纯INS的车载导航系统，虽然这种周期振荡会导致导航位置出现偏差，但它的位置依然是连续变化的。因此，对于INS来说，可以引入一个平移向量，在每次进行匹配候选道路搜索之前，都对当前INS输出的位置进行补偿，使补偿之后的位置更加接近真实的位置，进而缩小搜索候选道路的范围，节省时间，提高效率。另外，候选道路的选择范围和INS返回的速率相关，速率大，选择范围变大；速率小，选择范围变小。

 图 1 平移向量对INS位置的补偿 Fig.1 Compensation of location based on translation vector

1.2 基于动态权值的道路选择方法

 $\gamma = \frac{{(x - {x_1})({x_2} - {x_1}) + (y - {y_1})({y_2} - {y_1})}}{{{{({x_1} - {x_2})}^2} + {{({y_1} - {y_2})}^2}}}$ (1)
 图 2 距离d与γ的定义 Fig.2 The definition of distance d and γ

 图 3 INS航向与道路的夹角β Fig.3 Angle β between INS and road

 图 4 道路网络的拓扑结构 Fig.4 Topological structure of road network

 ${f_i} = {\omega _1}\left( {\sum\limits_{k = 1}^n {{d_k} - {d_i}} } \right)/\sum\limits_{k = 1}^n {{d_k} + {\omega _2}{\theta _i}}$ (2)

1.3 基于平移向量的INS位置修正方法

 $\begin{array}{l} {x_i} = \frac{{({x_2} - {x_1})[x({x_2} - {x_1}) + y({y_2} - {y_1})] + ({y_2} - {y_1})({x_1}{y_2} - {x_2}{y_1})}}{{{{({x_2} - {x_1})}^2} + {{({y_2} - {y_1})}^2}}}\\ {y_i} = \frac{{({y_2} - {y_1})[x({x_2} - {x_1}) + y({y_2} - {y_1})] - ({x_2} - {x_1})({x_1}{y_2} - {x_2}{y_1})}}{{{{({x_2} - {x_1})}^2} + {{({y_2} - {y_1})}^2}}} \end{array}$ (3)

 图 5 算法流程图 Fig.5 Flow chart of algorithm
2 跑车试验验证 2.1 试验条件

 图 6 跑车线路图 Fig.6 Route of driving car
2.2 试验结果

 图 7 跑车试验结果 Fig.7 Experimental result of driving car

 图 8 地图匹配前INS位置相对于参考位置的距离和地图匹配后INS位置相对于参考位置的距离 Fig.8 Distance before MM between INS and reference position and distance after MM between INS and reference position

3 结论

1) 多动态权值的道路选择方法、改进的投影修正方法、平移向量和转弯延迟的引入，使地图匹配算法不仅可以消除INS与道路垂直的横向位置误差和沿着道路的纵向位置误差，且在车辆航向角变化较小的情况下，仍然具有很高的匹配精度，这为基于地图匹配信息的INS的误差校正奠定了基础。

2) INS可以输出车辆的高度信息和姿态信息，如何利用该信息辅助地图匹配算法，提高匹配精度和效率，可以作为本文进一步研究的重点。

 [1] LI L, QUDDUS M, LIN Z. High accuracy tightly-coupled integrity monitoring algorithm for map-matching[J]. Transportation research part C:emerging technologies, 2013, 36: 13-26. DOI:10.1016/j.trc.2013.07.009 (0) [2] BIERLAIRE M, CHEN J, NEWMAN J. A probabilistic map matching method for smartphone GPS data[J]. Transportation research part C:emerging technologies, 2013, 26: 78-98. DOI:10.1016/j.trc.2012.08.001 (0) [3] REN M, KARIMI H A. Movement pattern recognition assisted map matching for pedestrian/wheelchair navigation[J]. The journal of navigation, 2012, 65(4): 617-633. DOI:10.1017/S0373463312000252 (0) [4] BLAZQUEZ C, MIRANDA P, PONCE A. Performance of a new enhanced topological decision-rule map-matching algorithm for transportation application[J]. Journal of applied research and technology, 2012, 10(6): 929-940. (0) [5] NASSREDDINE G, ABDALLAH F, DENOEUX T. Map matching algorithm using analysis and Dempster-Shafer theory[C]//2009 IEEE Intelligent Vehicles Symposium, 2009:494-499. (0) [6] CHU H J, TSAI G J, CHIANG K W, et al. GPS/MEMS INS data fusion and map matching in urban areas[J]. Sensors, 2013, 13(9): 11280-11288. DOI:10.3390/s130911280 (0) [7] SMAILI C, EI NAJJAR M E B, CHARPILLET F. A hybrid Bayesian framework for map matching formulation using switching kalman filter[J]. Journal of intelligent & robotic systems, 2014, 74(3): 725-743. (0) [8] ZHAO C Z, LI S L, LENG Y X. A new map matching algorithm for In-vehicle Inertial Navigation Systems[C]//2010 Second ⅡTA International Conference on Geoscience and Remote Sensing. Shandong, China, 2010:590-592. (0) [9] 龚柏春, 罗建军, 李岁劳, 等. 基于移动相关的最小二乘法地图匹配新算法[J]. 中国惯性技术学报, 2012, 20(4): 435-439. GONG Baichun, LUO Jianjun, LI Suilao, et al. Novelmap-matching algorithm based on moving-related least squares[J]. Journal of Chinese inertial technology, 2012, 20(4): 435-439. (0) [10] LUO H, DENG Z H, FU M Y, et al. A map matching algorithm for Inertial Navigation Systems based on the adaptive projection method[C]//2014 Seventh International Symposium on Computational Intelligence and Design. Hangzhou, China, 2014:304-308. (0)