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Pedestrian Inertial Navigation: An Overview of Model and Data-Driven Approaches (2407.21676v1)

Published 31 Jul 2024 in cs.RO and eess.SP

Abstract: The task of indoor positioning is fundamental to several applications, including navigation, healthcare, location-based services, and security. An emerging field is inertial navigation for pedestrians, which relies only on inertial sensors for positioning. In this paper, we present inertial pedestrian navigation models and learning approaches. Among these, are methods and algorithms for shoe-mounted inertial sensors and pedestrian dead reckoning (PDR) with unconstrained inertial sensors. We also address three categories of data-driven PDR strategies: activity-assisted, hybrid approaches, and learning-based frameworks.

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References (67)
  1. F. Liu, J. Liu, Y. Yin, W. Wang, D. Hu, P. Chen, and Q. Niu, “Survey on WiFi-based indoor positioning techniques,” IET communications, vol. 14, no. 9, pp. 1372–1383, 2020.
  2. C. Yang and H.-R. Shao, “WiFi-based indoor positioning,” IEEE Communications Magazine, vol. 53, no. 3, pp. 150–157, 2015.
  3. R. Kumar, J. Torres-Sospedra, and V. K. Chaurasiya, “H2lwrf-pdr: An efficient indoor positioning algorithm using a single wi-fi access point and pedestrian dead reckoning,” Internet of Things, p. 101271, 2024.
  4. R. F. Brena, J. P. García-Vázquez, C. E. Galván-Tejada, D. Muñoz-Rodriguez, C. Vargas-Rosales, J. Fangmeyer et al., “Evolution of indoor positioning technologies: A survey,” Journal of Sensors, vol. 20, 2017.
  5. D. Khan, Z. Cheng, H. Uchiyama, S. Ali, M. Asshad, and K. Kiyokawa, “Recent advances in vision-based indoor navigation: A systematic literature review,” Computers & Graphics, vol. 104, pp. 24–45, 2022.
  6. Z. Zhou, W. Feng, P. Li, Z. Liu, X. Xu, and Y. Yao, “A fusion method of pedestrian dead reckoning and pseudo indoor plan based on conditional random field,” Measurement, vol. 207, p. 112417, 2023.
  7. R. Harle, “A survey of indoor inertial positioning systems for pedestrians,” IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1281–1293, 2013.
  8. L. Fang, P. J. Antsaklis, L. A. Montestruque, M. B. McMickell, M. Lemmon, Y. Sun, H. Fang, I. Koutroulis, M. Haenggi, M. Xie et al., “Design of a wireless assisted pedestrian dead reckoning system-the NavMote experience,” IEEE transactions on Instrumentation and Measurement, vol. 54, no. 6, pp. 2342–2358, 2005.
  9. K. Tumkur and S. Subbiah, “Modeling human walking for step detection and stride determination by 3-axis accelerometer readings in pedometer,” in 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation, 2012, pp. 199–204.
  10. J. Park, Y. Kim, and J. Lee, “Waist mounted pedestrian dead-reckoning system,” in 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2012, pp. 335–336.
  11. J. Seo, Y. Chiang, T. H. Laine, and A. M. Khan, “Step counting on smartphones using advanced zero-crossing and linear regression,” in Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, 2015, pp. 1–7.
  12. J. Santos, A. Costa, and M. J. Nicolau, “Autocorrelation analysis of accelerometer signal to detect and count steps of smartphone users,” in 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN).   IEEE, 2019, pp. 1–7.
  13. M. B. Rhudy and J. M. Mahoney, “A comprehensive comparison of simple step counting techniques using wrist-and ankle-mounted accelerometer and gyroscope signals,” Journal of Medical Engineering and Technology, vol. 42, no. 3, pp. 236–243, 2018.
  14. A. C. Dirican and S. Aksoy, “Step counting using smartphone accelerometer and fast fourier transform,” Sigma J. Eng. Nat. Sci, vol. 8, pp. 175–182, 2017.
  15. J.-H. Wang, J.-J. Ding, Y. Chen, and H.-H. Chen, “Real time accelerometer-based gait recognition using adaptive windowed wavelet transforms,” in 2012 ieee asia pacific conference on circuits and systems.   IEEE, 2012, pp. 591–594.
  16. M. Vezočnik and M. B. Juric, “Average step length estimation models’ evaluation using inertial sensors: A review,” IEEE sensors journal, vol. 19, no. 2, pp. 396–403, 2018.
  17. R. Soni and S. Trapasiya, “A survey of step length estimation models based on inertial sensors for indoor navigation systems,” International Journal of Communication Systems, vol. 35, no. 4, p. e5053, 2022.
  18. A. R. Pratama, Widyawan, and R. Hidayat, “Smartphone-based pedestrian dead reckoning as an indoor positioning system,” in 2012 International Conference on System Engineering and Technology (ICSET), 2012, pp. 1–6.
  19. H. Weinberg, “Using the ADXL202 in pedometer and personal navigation applications,” Analog Devices AN-602 application note, vol. 2, no. 2, pp. 1–6, 2002.
  20. I. Klein and O. Asraf, “Stepnet—deep learning approaches for step length estimation,” IEEE Access, vol. 8, pp. 85 706–85 713, 2020.
  21. S. H. Shin and C. G. Park, “Adaptive step length estimation algorithm using optimal parameters and movement status awareness,” Medical Engineering and Physics, vol. 33, no. 9, pp. 1064–1071, 2011.
  22. D. Choukroun, I. Y. Bar-Itzhack, and Y. Oshman, “Novel quaternion Kalman filter,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 1, pp. 174–190, 2006.
  23. J. K. Lee, E. J. Park, and S. N. Robinovitch, “Estimation of attitude and external acceleration using inertial sensor measurement during various dynamic conditions,” IEEE transactions on instrumentation and measurement, vol. 61, no. 8, pp. 2262–2273, 2012.
  24. A. M. Sabatini, “Kalman-filter-based orientation determination using inertial/magnetic sensors: Observability analysis and performance evaluation,” Sensors, vol. 11, no. 10, pp. 9182–9206, 2011.
  25. R. Munguia and A. Grau, “Attitude and heading system based on EKF total state configuration,” in 2011 IEEE International Symposium on Industrial Electronics.   IEEE, 2011, pp. 2147–2152.
  26. R. Mahony, T. Hamel, and J.-M. Pflimlin, “Nonlinear complementary filters on the special orthogonal group,” IEEE Transactions on automatic control, vol. 53, no. 5, pp. 1203–1218, 2008.
  27. H. Fourati, N. Manamanni, L. Afilal, and Y. Handrich, “A nonlinear filtering approach for the attitude and dynamic body acceleration estimation based on inertial and magnetic sensors: Bio-logging application,” IEEE Sensors Journal, vol. 11, no. 1, pp. 233–244, 2010.
  28. S. O. Madgwick, A. J. Harrison, and R. Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” in 2011 IEEE international conference on rehabilitation robotics.   IEEE, 2011, pp. 1–7.
  29. E. Vertzberger and I. Klein, “Attitude adaptive estimation with smartphone classification for pedestrian navigation,” IEEE Sensors Journal, vol. 21, no. 7, pp. 9341–9348, 2021.
  30. ——, “Attitude and heading adaptive estimation using a data driven approach,” in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN).   IEEE, 2021, pp. 1–6.
  31. ——, “Adaptive attitude estimation using a hybrid model-learning approach,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–9, 2022.
  32. R. Leonardo, G. Rodrigues, M. Barandas, P. Alves, R. Santos, and H. Gamboa, “Determination of the walking direction of a pedestrian from acceleration data,” in 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN).   IEEE, 2019, pp. 1–6.
  33. Z.-A. Deng, G. Wang, Y. Hu, and D. Wu, “Heading estimation for indoor pedestrian navigation using a smartphone in the pocket,” Sensors, vol. 15, no. 9, pp. 21 518–21 536, 2015.
  34. A. Manos, I. Klein, and T. Hazan, “Gravity direction estimation and heading determination for pedestrian navigation,” in 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN).   IEEE, 2018, pp. 206–212.
  35. V. Thio, K. B. Ånonsen, and J. K. Bekkeng, “Relative heading estimation for pedestrians based on the gravity vector,” IEEE Sensors Journal, vol. 21, no. 6, pp. 8218–8225, 2021.
  36. A. Manos, T. Hazan, and I. Klein, “Walking direction estimation using smartphone sensors: A deep network-based framework,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–12, 2022.
  37. Q. Wang, H. Luo, L. Ye, A. Men, F. Zhao, Y. Huang, and C. Ou, “Pedestrian heading estimation based on spatial transformer networks and hierarchical LSTM,” IEEE Access, vol. 7, pp. 162 309–162 322, 2019.
  38. A. Manos, I. Klein, and T. Hazan, “Gravity-based methods for heading computation in pedestrian dead reckoning,” Sensors, vol. 19, no. 5, p. 1170, 2019.
  39. Y.-K. Kim, S.-H. Choi, H.-W. Kim, and J.-M. Lee, “Performance improvement and height estimation of pedestrian dead-reckoning system using a low cost MEMS sensor,” in 2012 12th International Conference on Control, Automation and Systems, 2012, pp. 1655–1660.
  40. S. Asano, Y. Wakuda, N. Koshizuka, and K. Sakamura, “A robust pedestrian dead-reckoning positioning based on pedestrian behavior and sensor validity,” in Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium, 2012, pp. 328–333.
  41. S. Boim, G. Even-Tzur, and I. Klein, “Height difference determination using smartphones based accelerometers,” IEEE Sensors Journal, vol. 22, no. 6, pp. 4908–4915, 2021.
  42. K. Itzik and L. Yaakov, “Step-length estimation during movement on stairs,” in 2019 27th Mediterranean Conference on Control and Automation (MED).   IEEE, 2019, pp. 518–523.
  43. E. Foxlin, “Pedestrian tracking with shoe-mounted inertial sensors,” IEEE Computer Graphics and Applications, vol. 25, no. 6, pp. 38–46, 2005.
  44. J.-O. Nilsson, I. Skog, P. Händel, and K. Hari, “Foot-mounted INS for everybody-an open-source embedded implementation,” in Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.   Ieee, 2012, pp. 140–145.
  45. D. Engelsman and I. Klein, “Information-aided inertial navigation: A review,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–18, 2023.
  46. C. Fischer, P. T. Sukumar, and M. Hazas, “Tutorial: Implementing a pedestrian tracker using inertial sensors,” IEEE pervasive computing, vol. 12, no. 2, pp. 17–27, 2012.
  47. A. R. Jiménez, F. Seco, J. C. Prieto, and J. Guevara, “Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted imu,” in 2010 7th workshop on positioning, navigation and communication.   IEEE, 2010, pp. 135–143.
  48. J. Wahlström and I. Skog, “Fifteen years of progress at zero velocity: A review,” IEEE Sensors Journal, vol. 21, no. 2, pp. 1139–1151, 2021.
  49. I. Skog, P. Handel, J.-O. Nilsson, and J. Rantakokko, “Zero-velocity detection—an algorithm evaluation,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 11, pp. 2657–2666, 2010.
  50. S. Y. Park, H. Ju, and C. G. Park, “Stance phase detection of multiple actions for military drill using foot-mounted IMU,” sensors, vol. 14, p. 16, 2016.
  51. B. Wagstaff, V. Peretroukhin, and J. Kelly, “Robust data-driven zero-velocity detection for foot-mounted inertial navigation,” IEEE Sensors Journal, vol. 20, no. 2, pp. 957–967, 2019.
  52. A. Etzion and I. Klein, “MoRPI: Mobile robot pure inertial navigation,” IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 141–150, 2023.
  53. Y. Li, R. Chen, X. Niu, Y. Zhuang, Z. Gao, X. Hu, and N. El-Sheimy, “Inertial sensing meets machine learning: Opportunity or challenge?” IEEE Transactions on Intelligent Transportation Systems, 2021.
  54. I. Klein, “Data-driven meets navigation: Concepts, models, and experimental validation,” in 2022 DGON Inertial Sensors and Systems (ISS).   IEEE, 2022, pp. 1–21.
  55. C. Chen and X. Pan, “Deep learning for inertial positioning: A survey,” arXiv preprint arXiv:2303.03757, 2023.
  56. N. Cohen and I. Klein, “Inertial navigation meets deep learning: A survey of current trends and future directions,” arXiv preprint arXiv:2307.00014, 2023.
  57. Q. Wang, M. Fu, J. Wang, H. Luo, L. Sun, Z. Ma, W. Li, C. Zhang, R. Huang, X. Li et al., “Recent advances in pedestrian inertial navigation based on smartphone: A review,” IEEE Sensors Journal, vol. 22, no. 23, pp. 22 319–22 343, 2022.
  58. M. Elhoushi, J. Georgy, A. Noureldin, and M. J. Korenberg, “A survey on approaches of motion mode recognition using sensors,” IEEE Transactions on intelligent transportation systems, vol. 18, no. 7, pp. 1662–1686, 2016.
  59. I. Klein, “Smartphone location recognition: A deep learning-based approach,” Sensors, vol. 20, no. 1, p. 214, 2019.
  60. I. Klein, Y. Solaz, and G. Ohayon, “Pedestrian dead reckoning with smartphone mode recognition,” IEEE Sensors Journal, vol. 18, no. 18, pp. 7577–7584, 2018.
  61. Q. Wang, H. Luo, J. Wang, L. Sun, Z. Ma, C. Zhang, M. Fu, and F. Zhao, “Recent advances in pedestrian navigation activity recognition: A review,” IEEE Sensors Journal, vol. 22, no. 8, pp. 7499–7518, 2022.
  62. N. Daniel, F. Goldberg, and I. Klein, “Smartphone location recognition with unknown modes in deep feature space,” Sensors, vol. 21, no. 14, p. 4807, 2021.
  63. F. Bo, J. Li, and W. Wang, “Mode-independent stride length estimation with IMUs in smartphones,” IEEE Sensors Journal, vol. 22, no. 6, pp. 5824–5833, 2022.
  64. C. Chen, X. Lu, A. Markham, and N. Trigoni, “IONet: Learning to cure the curse of drift in inertial odometry,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
  65. O. Asraf, F. Shama, and I. Klein, “PDRNet: A deep-learning pedestrian dead reckoning framework,” IEEE Sensors Journal, vol. 22, no. 6, pp. 4932–4939, 2021.
  66. H. Yan, Q. Shan, and Y. Furukawa, “RIDI: Robust IMU double integration,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 621–636.
  67. S. Herath, H. Yan, and Y. Furukawa, “RoNIN: Robust neural inertial navigation in the wild: Benchmark, evaluations, & new methods,” in 2020 IEEE international conference on robotics and automation (ICRA).   IEEE, 2020, pp. 3146–3152.

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