吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (3): 653-662.doi: 10.13229/j.cnki.jdxbgxb20221273
• 通信与控制工程 • 上一篇
Ke HE(),Hai-tao DING,Xuan-qi LAI,Nan XU,Kong-hui GUO
摘要:
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。
中图分类号:
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