A2DIO: Attention-Driven Deep Inertial Odometry for Pedestrian Localization based on 6D IMU

Abstract

In this work, we propose A2DIO, a novel hybrid neural network model with a set of carefully designed attention mechanisms for pose invariant inertial odometry. The key idea is to extract both local and global features from the window of IMU measurements for velocity prediction. A2DIO leverages the convolutional neural network (CNN) to capture the sectional features and long-short term memory (LSTM) recurrent neural network to extract long-range dependencies. In both CNN and LSTM modules, attention mechanisms are designed and embedded for better model representation. Specifically, in the CNN attention block, the convolved features are refined along both channel and spatial dimensions, respectively. For the LSTM module, softmax scoring is applied to update the weights of the hidden states along the temporal axis. We evaluate A2DIO on the benchmark with the largest and most natural IMU data, RoNIN. Extensive ablation experiments demonstrate the effectiveness of our A2DIO model. Compared with the state of the art, the 50th percentile accuracy of A2DIO is 18.21 % higher and the 90th percentile accuracy is 21.15 % higher for all the phone holders not appeared in the training set.