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A Method of Road Data Aided Inertial Navigation by Using Learning to Rank and iccp algorithm

Journal of Geodesy and Geoinformation Science 2021年第4期4卷 84-96页

作者： Xiang LI Yixin HUA Wenbing LIU Institute of Geospatial Information Information Engineering UniversityZhengzhou 450001China Army Logistics University of PLA Chongqing 401311China

As an independent navigation method,inertial navigation system(INS)has played a huge advantage in a lot of special *** its positioning error will accumulate with time,so it is difficult to work independently for a long *** vehicle loaded with the inertial navigation system usually drives on the road,so the high precision road data based on geographic information system(GIS)can be used as a bind of auxiliary information,which could correct INS errors by the correlation matching *** existing road matching methods rely on mathematical models,mostly for global positioning system(GPS)trajectory data,and are limited to model ***,based on the features of inertial navigation trajectory and road,this paper proposes a road data aided vehicle inertial navigation method based on the learning to rank and iterative closest contour point(iccp)***,according to the geometric and directional features of inertial navigation trajectory and road,the combined feature vector is constructed as the input value;Furthermore,the scoring function and RankNet neural network based on the features of vehicle trajectory data and road data are constructed,which can learn and extract the features;Then,the nearest point of each track point and its corresponding road data set to be matched is *** average translation between the two data sets is calculated by using the position relationship between each group of track points to be matched and road points;Finally,the trajectory data set is iteratively translated according to the translation amount,and the matching track point set is obtained when the trajectory error converges to complete the *** experiments,it is compared with other algorithms including the hidden Markov model(HMM)matching *** experimental results show that the algorithm can effectively suppress the divergence of trajectory *** matching accuracy is close to HMM algorithm,and the computational ef

关键词： iccp algorithm vehicle trajectory data features road matching pairwise learning

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