Zhu Yanping, Zhang Jianqiang, Chen Wenlong, Zhu Chenyang, Yan Sen, Chen Qi
School of Wang Zheng Microelectronics, Changzhou University, Changzhou 213159, China.
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213159, China.
Sensors (Basel). 2025 May 30;25(11):3441. doi: 10.3390/s25113441.
To address the challenges of accurate indoor positioning in complex environments, this paper proposes a two-stage indoor positioning method, ResT-IMU, which integrates the ResNet and Transformer architectures. The method initially processes the IMU data using Kalman filtering, followed by the application of windowing to the data. Residual networks are then employed to extract motion features by learning the residual mapping of the input data, which enhances the model's ability to capture motion changes and predict instantaneous velocity. Subsequently, the self-attention mechanism of the Transformer is utilized to capture the temporal features of the IMU data, thereby refining the estimation of movement direction in conjunction with the velocity predictions. Finally, a fully connected layer outputs the predicted velocity and direction, which are used to calculate the trajectory. During training, the RMSE loss is used to optimize velocity prediction, while the cosine similarity loss is employed for direction prediction. Theexperimental results demonstrate that ResT-IMU achieves velocity prediction errors of 0.0182 m/s on the iIMU-TD dataset and 0.014 m/s on the RoNIN dataset. Compared with the ResNet model, ResT-IMU achieves reductions of 0.19 m in ATE and 0.05 m in RTE on the RoNIN dataset. Compared with the IMUNet model, ResT-IMU achieves reductions of 0.61 m in ATE and 0.02 m in RTE on the iIMU-TD dataset and reductions of 0.32 m in ATE and 0.33 m in RTE on the RoNIN dataset. Compared with the ResMixer model, ResT-IMU achieves reductions of 0.13 m in ATE and 0.02 m in RTE on the RoNIN dataset. These improvements indicate that ResT-IMU offers superior accuracy and robustness in trajectory prediction.
为应对复杂环境中精确室内定位的挑战，本文提出了一种两阶段室内定位方法ResT-IMU，该方法集成了ResNet和Transformer架构。该方法首先使用卡尔曼滤波处理惯性测量单元（IMU）数据，然后对数据应用加窗处理。接着使用残差网络通过学习输入数据的残差映射来提取运动特征，这增强了模型捕捉运动变化和预测瞬时速度的能力。随后，利用Transformer的自注意力机制捕捉IMU数据的时间特征，从而结合速度预测细化运动方向估计。最后，全连接层输出预测的速度和方向，用于计算轨迹。在训练过程中，均方根误差（RMSE）损失用于优化速度预测，而余弦相似度损失用于方向预测。实验结果表明，ResT-IMU在iIMU-TD数据集上的速度预测误差为0.0182米/秒，在RoNIN数据集上为0.014米/秒。与ResNet模型相比，ResT-IMU在RoNIN数据集上的绝对轨迹误差（ATE）降低了0.19米，相对轨迹误差（RTE）降低了0.05米。与IMUNet模型相比，ResT-IMU在iIMU-TD数据集上的ATE降低了0.61米，RTE降低了0.02米，在RoNIN数据集上的ATE降低了0.32米，RTE降低了0.33米。与ResMixer模型相比，ResT-IMU在RoNIN数据集上的ATE降低了0.13米，RTE降低了0.02米。这些改进表明，ResT-IMU在轨迹预测方面具有更高的准确性和鲁棒性。
