Wheel odometry error prediction model based on transformer

To address the problem of unpredictable and variable errors when using wheel odometry for localization，a wheel odometry error prediction model based on Transformer neural network is developed to accurately predict the odometry error that accumulates and changes as the mileage increases，and to improve the accuracy of localization using wheel odometry under GPS occlusion. First，two models were established without and with the driving condition characteristics，then they were compared with the LSTM model under various driving conditions. The experimental results show that the Transformer-based wheel odometry error prediction model can accurately predict the odometry error with higher accuracy，stability and reliability than the LSTM model under both regular driving conditions and challenging driving conditions where it is difficult to measure the odometry signal accurately. At the same time，compared with the Transformer model without considering the driving condition characteristics，the Transformer model with considering the driving condition characteristics improve the performance in all evaluation indexes，which proves that considering the driving condition characteristics can effectively improve the prediction performance of the model. © 2023 Editorial Board of Jilin University. All rights reserved.