Attitude estimation based on multi-scale grouped spatio-temporal attention neural networks

• Autorzy: Rong, Hailong , Wu, Xiaohui , Wang, Hao , Jin, Tianlei , Zou, Ling
• Języki publikacji: EN

Abstrakty

EN

In recent years, due to the proliferation of inertial measurement units (IMUs) in mobile devices such as smartphones, attitude estimation using inertial and magnetic sensors has been the subject of considerable research. Traditional methods involve probabilistic and iterative state estimation; however, these approaches do not generalize well over continuously changing motion dynamics and environmental conditions. Therefore, this paper proposes a deep learning-based approach for attitude estimation. This approach segments data from sensors into different windows and estimates attitude by separately extracting local features and global features from sensor data using a residual network (ResNet18) and a long short-term memory network (LSTM). To improve the accuracy of attitude estimation, a multi-scale attention mechanism is designed within ResNet18 to capture finer temporal information in the sensor data. The experimental results indicate that the accuracy of attitude estimation using this method surpasses that of other methods proposed in recent years.

Słowa kluczowe

EN

MEMS; attitude estimation; deep learning; attention mechanism

• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2024
• Tom: Vol. 31, nr 1
• Strony: 195--211
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr., wzory

Twórcy

autor

Rong, Hailong

• Changzhou University, Changzhou 213164, China,

autor

Wu, Xiaohui

• Changzhou University, Changzhou 213164, China

autor

Wang, Hao

• Changzhou University, Changzhou 213164, China

autor

Jin, Tianlei

• Changzhou University, Changzhou 213164, China

autor

Zou, Ling

• Changzhou University, Changzhou 213164, China

Bibliografia

• [1] Groves, P. D. (2015). Navigation using inertial sensors. IEEE Aerospace and Electronic Systems Magazine, 30(9), 42-69. https://doi.org/10.1109/MAES.2014.130191
• [2] Brossard, M., Barrau, A. & Bonnabel, S. (2020). AI-IMU Dead-Reckoning. IEEE Transactions on Intelligent Vehicles, 5(4), 585-595. https://doi.org/10.1109/TIV.2020.2980758
• [3] Liu, W., Caruso, D., Ilg, E., Dong, J., Mourikis, A. I., Daniilidis, K., Kumar, V. & Engel, J. (2020). TLIO: tight learned inertial odometry. IEEE Robotics and Automation Letters, 5(4), 5653-5660. https://doi.org/10.1109/LRA.2020.3007421
• [4] Koksal, N., Jalalmaab, M. & Fidan, B. (2019). Adaptive linear quadratic attitude tracking control of a quadrotor UAV based on IMU sensor data fusion. Sensors, 19(1), 46. https://doi.org/10.3390/s19010046
• [5] Alo, U. R., Nweke, H. F., Teh, Y. W. & Murtaza, G. (2020). Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System. Sensors, 20(21), 6300. https://doi.org/10.3390/s20216300
• [6] Bhardwaj, R., Kumar, N. & Kumar, V. (2018). Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling. Transactions of the Institute of Measurement and Control, 40(9), 2843-2854. https://doi.org/10.1177/0142331217708237
• [7] Han, S., Meng, Z., Omisore, O., Akinyemi, T. & Yan, Y. (2020). Random error reduction algorithms for MEMS inertial sensor accuracy improvement - a review. Micromachines, 11(11), 1021. https://doi.org/10.3390/mi11111021
• [8] Brotchie, J., Li, W., Kealy, A. & Moran, B. (2021). Evaluating tracking rotations using maximal entropy distributions for smartphone applications. IEEE Access, 9, 168806-168815. https://doi.org/10.1109/ACCESS.2021.3135012
• [9] Quinchia, A. G., Falco, G., Falletti, E., Dovis, F. & Ferrer, C. (2013). A comparison between different error modeling of MEMS applied to GPS/INS integrated systems. Sensors, 13(8), 9549-9588. https://doi.org/10.3390/s130809549
• [10] Huang, L. (2015). Auto regressive moving average (ARMA) modeling method for gyro random noise using a robust Kalman filter. Sensors, 15(10), 25277-25286. https://doi.org/10.3390/s151025277
• [11] LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
• [12] Bilski, J., Rutkowski, L., Smoląg, J. & Tao, D. (2021). A novel method for speed training acceleration of recurrent neural networks. Information Sciences, 553, 266-279. https://doi.org/10.1016/j.ins.2020.10.025
• [13] Masumura, R., Asami, T., Oba, T., Sakauchi, S. & Ito, A. (2019). Latent words recurrent neural network language models for automatic speech recognition. IEICE Transactions on Information and Systems, E102D(12), 2557-2567. https://doi.org/10.1587/transinf.2018EDP7242
• [14] Cui, Q., Wu, S., Huang, Y. & Wang, L. (2019). A hierarchical contextual attention-based network for sequential recommendation. Neurocomputing, 358, 141-149. https://doi.org/10.1016/j.neucom.2019.04.073
• [15] Li, D., Zhou, J. & Liu, Y. (2021). Recurrent-neural-network-based unscented Kalman filter for estimating and compensating the random drift of MEMS gyroscopes in real time. Mechanical Systems and Signal Processing, 147, 107057. https://doi.org/10.1016/j.ymssp.2020.107057
• [16] Yang, D., Gu, C., Zhu, Y., Dai, B., Zhang, K., Zhang, Z. & Li, B. (2020). A concrete dam deformation prediction method based on LSTM with attention mechanism. IEEE Access, 8, 185177-185186. https://doi.org/10.1109/ACCESS.2020.3029562
• [17] Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
• [18] Pan, E., Mei, X., Wang, Q., Ma, Y. & Ma, J. (2020). Spectral-spatial classification for hyperspectral image based on a single GRU. Neurocomputing, 387, 150-160. https://doi.org/10.1016/j.neucom.2020.01.029
• [19] Jiang, C., Chen, S., Chen, Y., Zhang, B., Feng, Z., Zhou, H. & Bo, Y. (2018). A MEMS IMU de-noising method using long short term memory recurrent neural networks (LSTM-RNN). Sensors, 18(10), 3470. https://doi.org/10.3390/s18103470
• [20] Jiang, C., Chen, Y., Chen, S., Bo, Y., Li, W., Tian, W. & Guo, J. (2019). A mixed deep recurrent neural network for MEMS gyroscope noise suppressing. Electronics, 8(2), 181. https://doi.org/10.3390/electronics8020181
• [21] Esfahani, M. A., Wang, H., Wu, K. & Yuan, S. (2020). OriNet: Robust 3-D orientation estimation with a single particular IMU. IEEE Robotics and Automation Letters, 5(2), 399-406. https://doi.org/10.1109/LRA.2019.2959507
• [22] Bahdanau, D., Cho, K. & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv.Org. https://arxiv.org/abs/1409.0473v7
• [23] Shih, S.-Y., Sun, F.-K. & Lee, H. (2019). Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8-9), 1421-1441. https://doi.org/10.1007/s10994-019-05815-0
• [24] Wang, Y., Zhang, J., Kan, M., Shan, S. & Chen, X. (2020). Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12272-12281. https://doi.org/10.1109/CVPR42600.2020.01229
• [25] Chen, C., Lu, X., Markham, A. & Trigoni, N. (2018). IONet: Learning to cure the curse of drift in inertial odometry. Thirty-Second AAAI Conference on Artificial Intelligence, America, 6468-6476. https://ojs.aaai.org/index.php/AAAI/article/view/12102
• [26] Wang, Y., Cheng, H., Wang, C. & Meng, M. Q.-H. (2021). Pose-invariant inertial odometry for pedestrian localization. IEEE Transactions on Instrumentation and Measurement, 70, 8503512. https://doi.org/10.1109/TIM.2021.3093922
• [27] Huang, F., Wang, Z., Xing, L. & Gao, C. (2022). A MEMS IMU gyroscope calibration method based on deep learning. IEEE Transactions on Instrumentation and Measurement, 71, 1003009. https://doi.org/10.1109/TIM.2022.3160538
• [28] Narkhede, P., Walambe, R., Poddar, S. & Kotecha, K. (2021). Incremental learning of LSTM framework for sensor fusion in attitude estimation. Peerj Computer Science, 7, e662. https://doi.org/10.7717/peerj-cs.662
• [29] Brossard, M., Bonnabel, S. & Barrau, A. (2020). Denoising IMU gyroscopes with deep learning for open-loop attitude estimation. IEEE Robotics and Automation Letters, 5(3), 4796-4803. https://doi.org/10.1109/LRA.2020.3003256
• [30] Chen, C., Zhao, P., Lu, C., Wang, W., Markham, A. &Trigoni, N. (2020). Deep-learning-based pedestrian inertial navigation: methods, data set, and on-device inference. IEEE Internet of Things Journal, 7(5), 4431-4441. https://doi.org/10.1109/JIOT.2020.2966773
• [31] Herath, S., Yan, H. & Furukawa, Y. (2020). RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods. IEEE International Conference on Robotics and Automation (ICRA), 3146-3152. https://doi.org/10.1109/icra40945.2020.9196860
• [32] Huynh, D. Q. (2009). Metrics for 3D Rotations: comparison and analysis. Journal of Mathematical Imaging and Vision, 35(2), 155-164. https://doi.org/10.1007/s10851-009-0161-2
• [33] Seong, J.-H., Lee, S.-H., Yoon, K.-K. & Seo, D.-H. (2019). Ellipse coefficient map-based geomagnetic fingerprint considering azimuth angles. Symmetry-Basel, 11(5), 708. https://doi.org/10.3390/sym11050708
• [34] Ousaloo, H. S., Sharifi, G., Mahdian, J. & Nodeh, M. T. (2017). Complete Calibration of three-axis strapdown magnetometer in mounting frame. IEEE Sensors Journal, 17(23), 7886-7893. https://doi.org/10.1109/JSEN.2017.2766200

Identyfikatory

DOI

10.24425/mms.2024.148542