• Energy Research

Pedestrian inertial navigation technology plays an important role in indoor positioning technology. However, low-cost inertial sensors in smart devices are affected by bias and noise, resulting in rapidly increasing and accumulating errors when integrating double acceleration to obtain displacement. The data-driven class of pedestrian inertial navigation algorithms can reduce sensor bias and noise in IMU data by learning motion-related features through deep neural networks. Inspired by the RoNIN algorithm, this paper proposes a data-driven class algorithm, RBCN-Net. Firstly, the algorithm adds NAM and CBAM attention modules to the residual network ResNet18 to enhance the learning ability of the network for channel and spatial features. Adding the BiLSTM module can enhance the network’s ability to learn over long distances. Secondly, we construct a dataset VOIMU containing IMU data and ground truth trajectories based on visual inertial odometry (total distance of 18.53 km and total time of 5.65 h). Finally, the present algorithm is compared with CNN, LSTM, ResNet18 and ResNet50 networks in VOIMU dataset for experiments. The experimental results show that the RMSE values of RBCN-Net are reduced by 6.906, 2.726, 1.495 and 0.677, respectively, compared with the above networks, proving that the algorithm effectively improves the accuracy of pedestrian navigation.