Abstract
The lack of floor plans is one of the major obstacles to ubiquitous location-based services indoors. Dedicated mobile robots with high-precision sensors can scan and produce indoor maps, but the deployment remains low. Existing smartphone-based approaches usually adopt computer vision techniques to build the 3D point cloud, at the cost of extensive image collection efforts and the risk of privacy issues. In this paper, we propose BatMapper-Plus which constructs accurate and complete indoor floor plans by acoustic ranging and inertial sensing on smartphones. It employs acoustic signals to measure the distance to a nearby wall segment, and produces the accessible area by surrounding the building during walking. It also refines the constructed floor plan to eliminate scattered segments, and identifies connection areas including stairs and elevators among different floors. Extensive experiments in a teaching building and a residential building have shown our effectiveness compared with the state-of-the-art, without any privacy concerns and environmental limitations.
Keywords
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Wang, X., Marcotte, R.J., Olson, E.: Glfp: Global localization from a floor plan. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1627–1632 (2019)
Anderson, R., Curro, J.: Indoor navigation using convolutional neural networks and floor plans. In: Proceedings of ION GNSS+, pp. 2133–2150 (2021)
Harithas, S.S., Pardia, B.: Gennav: a generic indoor navigation system for mobile robots. In: Proceedings of IEEE I-SMAC, pp. 182–187 (2020)
Gao, R., et al.: Multi-story indoor floor plan reconstruction via mobile crowdsensing. IEEE Trans. Mob. Comput. 15(6), 1427–1442 (2016)
Zhou, B., Elbadry, M., Gao, R., Ye, F.: Batmapper: acoustic sensing based indoor floor plan construction using smartphones. In: Proceedings of ACM MobiSys, pp. 42–55 (2017)
Pradhan, S., Baig, G., Mao, W., Qiu, L., Chen, G., Yang, B.: Smartphone-based acoustic indoor space mapping. Proc. ACM on Interact. Mobile Wearable Ubiquit. Technol. 2(2), 1–26 (2018)
Gao, R., et al.: Glow in the dark: smartphone inertial odometry for vehicle tracking in gps blocked environments. IEEE Internet Things J. 8(16), 12955–12967 (2021)
Alzantot, M., Youssef, M.: Crowdinside: automatic construction of indoor floorplans. In: Proceedings of GIS, pp. 99–108 (2012)
Peng, Z., Gao, S., Xiao, B., Wei, G., Guo, S., Yang, Y.: Indoor floor plan construction through sensing data collected from smartphones. IEEE Internet Things J. 5(6), 4351–4364 (2018)
Luo, H., Zhao, F., Jiang, M., Ma, H., Zhang, Y.: Constructing an indoor floor plan using crowdsourcing based on magnetic fingerprinting. Sensors 17(11), 2678 (2017)
Chen, S., Li, M., Ren, K., Fu, X., Qiao, C.: Rise of the indoor crowd: reconstruction of building interior view via mobile crowdsourcing. In: Proceedings of ACM SenSys, pp. 59–71 (2015)
Philipp, D., et al.: Mapgenie: grammar-enhanced indoor map construction from crowd-sourced data. In: Proceedings of IEEE PerCom, pp. 139–147. IEEE (2014)
Mao, W., Sun, W., Wang, M., Qiu, L.: Deeprange: acoustic ranging via deep learning. Proceed. ACM Interact. Mobile Wearable Ubiquit. Technol. 4(4), 1–23 (2020)
Liu, Z., Chen, R., Feng Ye, F., Guo, G., Li, Z., Qian, L.: Improved toa estimation method for acoustic ranging in a reverberant environment. IEEE Sens. J. 2, 4844–4852 (2020)
Graham, D., Simmons, G., Nguyen, D.T., Zhou. G.: A software-based sonar ranging sensor for smart phones. IEEE Internet Things J. 2(6), 479–489, 2015
Zhang, H., Du, W., Zhou, P., Li, M., Mohapatra. P.: Dopenc: acoustic-based encounter profiling using smartphones. In: Proceedings of ACM MobiCom, pp. 294–307 (2016)
Shen, G., Chen, Z., Zhang, P., Moscibroda, T., Zhang, Y.: \(\{\)Walkie-Markie\(\}\): indoor pathway mapping made easy. In: Proceedings of USENIX NSDI, pp. 85–98 (2013)
Rai, A., Chintalapudi, K.K., Padmanabhan, V.N., Sen, R.: Zee: zero-effort crowdsourcing for indoor localization. In: Proceedings of ACM MobiCom, pp. 293–304 (2012)
Zhao, M., Ye, T., Gao, R., Ye, F., Wang, Y., Luo, G.: Vetrack: real time vehicle tracking in uninstrumented indoor environments. In: Proceedings of ACM SenSys, pp. 99–112 (2015)
Martínez del Horno, M., Orozco-Barbosa, L., García-Varea, I.: A smartphone-based multimodal indoor tracking system. Inf. Fusion 76, 36–45 (2021)
Wu, C., Zhang, F., Wang, B., Liu, K.J.R.: Easitrack: decimeter-level indoor tracking with graph-based particle filtering. IEEE Internet Things J. 7(3), 2397–2411, 2019
Gong, J., Zhang, X., Yuanjun Huang, J., Zhang, R.Y.: Robust inertial motion tracking through deep sensor fusion across smart earbuds and smartphone. Proc. ACM Interact. Mobile Wearable Ubiquit. Technol. 5(2), 1–26 (2021)
Alloulah M., Tuominen, L.: Imulet: a cloudlet for inertial tracking. In: Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications, pp. 50–56 (2021)
Acknowledgments
This work was supported in part by the Fundamental Research Funds for the Central Universities 2021JBM029, NSFC under Grant 62072029 and Grant 61872027, Beijing NSF Grant L192004, DiDi Research Collaboration Plan, and OPPO Research Fund.
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Meng, C., Jiang, S., Wu, M., Xiao, X., Tao, D., Gao, R. (2022). BatMapper-Plus: Smartphone-Based Multi-level Indoor Floor Plan Construction via Acoustic Ranging and Inertial Sensing. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_13
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DOI: https://doi.org/10.1007/978-3-031-19214-2_13
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