Abstract
We present a human-sensor interaction approach for indoor navigation, where we incorporate inertial motion unit sensors, human knowledge and human-computer interaction into the navigation process. The algorithm uses semantic representations of navigational constraints such as walls, stairs, and elevators, to correct the trajectory. The objective is to reduce the IMU drifting errors. The navigation prototype is implemented on a helmet with a holographic screen that can mix the actual visible image with mapping and visualization information, voice command and tactile interface. The helmet is to assist first responders in emergency environments of fire, flood, shooting, cyberattack, and medical distress, where GSP, cellular and regular WiFi is not available. The results show that the interactive navigation reduces drifting errors and it is an affordable alternative to existing technologies such as ultrasound, RFID, UWB radios, WiFi signatures, and camera-based SLAM (simultaneous localization and mapping) algorithms where matching features are not sufficient, especially in a dark or smoking environment.
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References
Lin, Q., An, Z., Yang, L.: Robooting ultrasonic positioning systems for ultrasound-incapable smart devices. https://arxiv.org/pdf/1812.02349.pdf
Indoor positioning via LoRaWAN, indoornavigation.com. https://www.indoornavigation.com/wiki-en/indoor-positioning-via-lorawan
WiFi positioning system, WikiPedia. https://en.wikipedia.org/wiki/Wi-Fi_positioning_system
Zafari, F., Gkelias, A., Leung, K.K.: A survey of indoor localization systems and technologies. arXiv: https://arxiv.org/pdf/1709.01015.pdf
Wang, Q., Lou, H., Men, A., Zhao, F.: An infrastructure-free indoor localization algorithm on smartphone. Sensors 18(10), 3317: https://www.researchgate.net/publication/328067193_An_Infrastructure-Free_Indoor_Localization_Algorithm_for_Smartphones
Noh, Y., Yamaguchi, H., Lee, U.: Infrastructure-free collaborative indoor positioning schema for time-critical team operations. IEEE Trans. SMC. 48(3) (2018). https://ieeexplore.ieee.org/abstract/document/7747408
SLAM, WikiPedia. https://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping
Mathworks. Implement SLAM with Lidar Scans (2020). https://www.mathworks.com/help/nav/ug/implement-simultaneous-localization-and-mapping-with-lidar-scans.html
Shin, Y.S., Kim, A.: Sparse depth enhanced direct thermal-infrared SLAM beyond the visible spectrum. arXiv:1902.10892. https://arxiv.org/abs/1902.10892
Barrie, D.: Supernavigators: exploring the wonders of how animals find their way. The Experiment, LLC (2019)
Sato, D., Oh, U., Naito, K., Takagi, H., Kitani, K., Asakawa, C.: NavCog3: an evaluation of a smartphone-based blind indoor navigation assistant with semantic features in a large-scale environment. In: ASSET 2017, Oct. 29-Nov. 1, Baltimore, MD, USA (2017). https://www.ri.cmu.edu/wp-content/uploads/2018/01/p270-sato.pdf
Cai, Y., Hackett, S., Alber, F.: Heads-Up LiDAR Imaging, to appear on IS&T, Electronic Imaging Conference, 20 January 2020
RTIMULib for Raspberry Pi on Github: https://github.com/RPi-Distro/RTIMULi
Acknowledgments
This work was performed under the financial assistance award 70NANB17H173 from U.S. Department of Commerce, National Institute of Standards and Technology, PSCR Division and PSIA Program. This project is also funded in part by Carnegie Mellon University’s Mobility21 National University Transportation Center, which is sponsored by the US Department of Transportation. The authors are grateful to the NIST PSCR Program Manager Jeb Benson for his comments and suggestions about the technical development of the hyper-reality helmet system.
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Cai, Y., Hackett, S., Alber, F. (2020). Interactive Indoor Localization on Helmet. In: Ahram, T., Falcão, C. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1217. Springer, Cham. https://doi.org/10.1007/978-3-030-51828-8_71
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DOI: https://doi.org/10.1007/978-3-030-51828-8_71
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