基于Transformer的轮式里程计误差预测模型

Wheel odometry error prediction model based on transformer

针对利用轮式里程计定位时会产生难以预测和多变误差的问题,提出了使用Transformer神经网络建立轮式里程计误差预测模型,以准确预测变化的里程误差,提高了轮式里程计的定位精度。首先,建立不考虑工况特征和考虑工况特征两种模型。然后,在多种工况下与LSTM模型进行对比实验,结果表明:在常规和挑战性工况下,本文模型相比LSTM模型具有更高的精度、稳定性和可靠性。同时,相比于不考虑工况特征,考虑工况特征能有效提高模型的整体性能。

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.