Research on crowd-sourced map road factor fusion discriminant clustering method
high-precision positioning alone is costly and slow to update,so it does not apply to a large range,while simultaneous localization and mapping(SLAM)alone will face high cumulative errors and slow updates.This paper uses a crowd-sourced method to produce high-precision maps,including two modules of SLAM and cloud map learning,and is committed to building key capabilities of multi-source heterogeneous maps The traditional clustering method is prone to cluster errors when the input such as data offset is not good.Therefore,based on the traditional DBSCAN clustering algorithm,this paper optimizes the core seed clustering mode,analyzes the data characteristics of various road elements,flexibly uses different discriminating elements to replace the traditional distance determination,and conducts extensive experiments on the crowdsourced data set collected by Changan Automobile.Compared with the traditional methods,our method is more suitable for the comprehensive clustering of road elements,is not limited by road types,and is more in line with the actual engineering needs.
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