Multi-sensor fusion for navigation technology and trajectory prediction under urban roads

doi:10.13474/j.cnki.11-2246.2019.0349

Abstract

Abstract: Easy access to accurate and reliable trajectory data is the key to the intelligent development of transportation, tourism and other industries. In view of this, a multi-source vehicle integrated navigation system integrating GPS, SINS and OBD is designed to collect trajectory data. The FGP data fusion method is proposed to compensate the accumulated position error caused by sensor noise, and to predict the track position information when the GPS outage. To effectively avoid error accumulation, the INS error compensation model is established through combination of Gradient Boosting and Decision Tree, and the regression parameters of the model are optimized by Particle Swarm Optimization in this method. The GPS, SINS and OBD data fusion is realized by exploiting federal filter thereby improving the accuracy of trajectory information. The actual road test proves that the GSO device based on the FGP can collect continuous and accurate trajectory data under various road conditions.

Key words: trajectory prediction, sensor noise, integrated navigation, data fusion

CLC Number:

P228

LI Tong, ZHANG Huibing, LIU Dingke, DAI Yujun, WU Dongqiang. Multi-sensor fusion for navigation technology and trajectory prediction under urban roads[J]. Bulletin of Surveying and Mapping, 2019, 0(11): 44-50.

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