The existing integrated navigation algorithms suffer from the problem of error divergence over time in complex satellite signal environments.To improve the navigation accuracy by adding dif-ferent auxiliary strategies for different driving scenarios,an integrated navigation supported by XGBoost algorithm for vehicle scene recognition is proposed.Firstly,features are constructed based on satellite navigation data and vehicle status data during driving,and the distribution differ-ences of features in different vehicle scenarios are compared by Kruskal-Wallis test;secondly,the XGBoost algorithm is used to fit the preprocessed data and obtain a vehicle scene recognition mod-el;finally,when the underground storage scenario is recognized,the change in heading angle is calculated by mechanical arrangement,the wheel speed kinematic model is also used to calculate the change in heading angle,and then the average of the changes in heading angle calculated by the two methods is calculated.The current attitude is recalculated,then the speed and position are up-dated with the new attitude.The experimental results show that in the underground storage sce-nario,compared to the algorithm without adding wheel speed kinematic assistance,the standard deviation of the heading accuracy of the integrated navigation algorithm with added assistance in-creases by an average of 55.27％.
Integrated navigationXGBoost algorithmVehicle scene recognitionKruskal-Wallis testWheel speed kinematic model
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