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

Wang，Yingying1; Cheng，Hu1; Meng，Max Q.H.2,3,4

10.1109/ICRA46639.2022.9811714

2022

1050-4729

978-1-7281-9682-4

Proceedings - IEEE International Conference on Robotics and Automation

819-825

23-27 May 2022

Philadelphia, PA, USA

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.

Training Location awareness Recurrent neural networks Automation Benchmark testing Feature extraction Convolutional neural networks

Others

English

2-s2.0-85136326296

Scopus

1.The Chinese University of Hong Kong,N.T.,Robotics,Perception and Artificial Intelligence Lab,Electronic Engineering Department,Hong Kong SAR,Hong Kong
2.Shenzhen Key Laboratory of Robotics Perception and Intelligence,Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Department of Electronic Engineering,The Chinese University of Hong Kong,Hong Kong,Hong Kong
4.Shenzhen Research Institute of the Chinese University of Hong Kong,Shenzhen,518057,China

Wang，Yingying,Cheng，Hu,Meng，Max Q.H.. A2DIO: Attention-Driven Deep Inertial Odometry for Pedestrian Localization based on 6D IMU[C],2022:819-825.