Spatiotemporal Co-Attention Hybrid Neural Network for Pedestrian Localization Based on 6D IMU

In this paper, we propose spatiotemporal co-attention hybrid neural network (SC-HNN), a novel hybrid neural network model with both spatial and temporal attention mechanisms for pose-invariant inertial odometry. The main idea is to extract both local and global features from a window of IMU measurements for velocity prediction. SC-HNN leverages the convolutional neural network (CNN) to capture the sectional features and long short-term memory (LSTM) recurrent neural network (RNN) to extract the long-range dependencies. Attention mechanisms are designed and embedded in both CNN and LSTM modules for better model representation. Specifically, in the CNN attention block, the convolved features are refined along both channel and element dimensions. For the LSTM module, softmax scoring is applied to update the weights of the hidden states along the temporal axis. We evaluate SC-HNN on the benchmark with the largest and most natural IMU data, RoNIN. Extensive ablation experiments demonstrate the effectiveness of our SC-HNN model. Compared with the state of the art, the 50th percentile accuracy of SC-HNN is 18.21% higher and the 90th percentile accuracy is 21.15% higher for all the phone holders not appeared in the training set. The real scenario inertial tracking trials in the CUHK campus further prove the superior generalization ability of the SC-HNN model.