Abstract
With the proliferation of smartphones, recent years have witnessed the rapid development of smartphone-based indoor navigation systems. However, existing solutions either bear high deployment cost or cannot support large-scale navigation. A scalable and plug-and-play indoor navigation system is still highly desirable. In this paper, we propose DeepNav, a new indoor navigation system that fully uses visual CNN to realize large-scale navigation. DeepNav adopts a single-pilot deployment scheme to realize fast deployment. It divides the indoor area into dense sub-areas to simplify image-based location matching while ensuring reasonable resolution. Practical realization of DeepNav entails a set of key challenges, e.g., invalid image recognition, classification of thousands of labels and under-fitting. In order to solve these challenges, we propose invalid image filter, subgroup sigmoid layer and movable object filter, respectively, for DeepNav. Finally, we implement a prototype of DeepNav on commercial smartphones. Experimental results demonstrate that DeepNav can be quickly deployed (e.g., within an hour in a 4-storey building) with an average localization error of 2.3 meters.
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Zheng Y, Shen G, Li L, Zhao C, Li M, Zhao F (2017) Travi-navi: Self-deployable indoor navigation system. IEEE/ACM Trans Netw 25(5):2655
Wang J, Zhang X, Gao Q, Yue H, Wang H (2017) Device-free wireless localization and activity recognition: A deep learning approach. IEEE Trans Veh Technol PP(99):1
Abdelnasser H, Mohamed R, Elgohary A, Alzantot M, Wang H, Sen S, Choudhury RR, Youssef M (2016) SemanticSLAM: Using environment landmarks for unsupervised indoor localization. IEEE Trans Mob Comput 15(7):1770
Alzantot M, Youssef M (2012) Crowdinside: automatic construction of indoor floorplans. In: Proceedings of the 20th international conference on advances in geographic information systems. ACM, pp 99–108
Yang Z, Wu C, Liu Y (2012) Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of the 18th annual international conference on mobile computing and networking. ACM, pp 269–280
Chen S, Li M, Ren K, Fu X, Qiao C (2015) Rise of the indoor crowd: Reconstruction of building interior view via mobile crowdsourcing. In: Proceedings of the 13th ACM conference on embedded networked sensor systems. ACM, pp 59–71
Gao R, Zhao M, Ye T, Ye F, Wang Y, Bian K, Wang T, Li X (2014) Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of the 20th annual international conference on Mobile computing and networking. ACM, pp 249–260
Chen S, Li M, Ren K, Qiao C (2015) Crowd map: Accurate reconstruction of indoor floor plans from crowdsourced sensor-rich videos. In: 2015 IEEE 35th International conference on distributed computing systems. IEEE, pp 1–10
Mur-Artal R, Montiel JMM, Tardos JD (2015) ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans Robot 31(5):1147
Dong J, Noreikis M, XIAO Y, Yla-Jaaski A (2018) ViNav: A vision-based indoor navigation system for smartphones. IEEE Trans Mob Comput
Shu Y, Kang GS, He T, Chen J (2015) Last-mile navigation using smartphones. In: International conference on mobile computing and networking, pp 512–524
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861
Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S, et al. (2017) Speed/accuracy trade-offs for modern convolutional object detectors. In: IEEE CVPR, vol 4
Ma N, Zhang X, Zheng HT, Sun J (2018) Shufflenet v2: Practical guidelines for efficient cnn architecture design. arXiv:1807.11164
Wei Y, Xia W, Lin M, Huang J, Ni B, Dong J, Zhao Y, Yan S (2015) HCP: A flexible CNN framework for multi-label image classification. IEEE Trans Pattern Anal Mach Intell 38(9):1901
Lin TY, Goyal P, Girshick R, He K, Dollár P. (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Google (2018) Tensorflow. https://www.tensorflow.org/
Yin Z, Wu C, Yang Z, Liu Y (2017) Peer-to-peer indoor navigation using smartphones. IEEE J Select Areas Commun PP(99):1
Yin Z, Wu C, Yang Z, Lane N, Liu Y (2017) ppNav: Peer-to-peer indoor navigation for smartphones. In: IEEE international conference on parallel and distributed systems, pp 104–111
Zhuang Y, Syed Z, Li Y, Elsheimy N (2016) Evaluation of two wifi positioning systems based on autonomous crowd sourcing on handheld devices for indoor navigation. IEEE Trans Mob Comput 15 (8):1982
Wang X, Gao L, Mao S, Pandey S (2017) CSI-Based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66(1):763
Zhang C, Subbu KP, Luo J, Wu J (2015) GROPING: Geomagnetism and crowdsensing powered indoor navigation. IEEE Trans Mob Comput 14(2):387
He S, Kang GS (2017) Geomagnetism for smartphone-based indoor localization: challenges, advances, and comparisons. Acm Comput Surv 50(6):1
Li Z, Shu Y, Karlsson BF, Lin Y, Moscibroda T (2017) Demo: Towards flexible and scalable indoor navigation. In: International conference on mobile computing and networking, pp 495–497
Liu Z, Zhang L, Liu Q, Yin Y, Cheng L, Zimmermann R (2017) Fusion of magnetic and visual sensors for indoor localization: infrastructure-free and more effective. IEEE Trans Multimed 19(4):874
Zhu Y, Mottaghi R, Kolve E, Lim JJ, Gupta A, Fei-Fei L, Farhadi A (2017) Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE international conference on robotics and automation (ICRA). IEEE, pp 3357–3364
Kahn G, Villaflor A, Ding B, Abbeel P, Levine S (2018) Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation. In: 2018 IEEE International conference on robotics and automation (ICRA). IEEE, pp 1–8
Shu Y, Bo C, Shen G, Zhao C, Li L, Zhao F (2015) Magicol: Indoor localization using pervasive magnetic field and opportunistic wifi sensing. IEEE J Select Areas Commun 33(7):1443
Li Y, Zhuang Y, Zhang P, Lan H, Niu X, El-Sheimy N (2017) An improved inertial/wifi/magnetic fusion structure for indoor navigation. Inf Fusion 34(C):101
Teng X, Guo D, Zhou X, Liu Z (2015) Poster: An indoor-outdoor navigation service for subway transportation systems. In: ACM Conference on embedded networked sensor systems, pp 415–416
Wu FJ (2018) A sensor-assisted emergency guiding system: sensor-centric or user-centric?. IEEE Trans Veh Technol 67(2):1598
Zhuang Y, Yang J, Qi L, Li Y, Cao Y, El-Sheimy N (2017) A pervasive integration platform of low-cost MEMS sensors and wireless signals for indoor localization. IEEE Internet Things J PP(99):1
Atia MM, Liu S, Nematallah H, Karamat TB, Noureldin A (2015) Integrated indoor navigation system for ground vehicles with automatic 3-d alignment and position initialization. IEEE Trans Veh Technol 64(4):1279
Tsirmpas C, Rompas A, Fokou O, Koutsouris D (2015) An indoor navigation system for visually impaired and elderly people based on Radio Frequency Identification (RFID). Inform Sci 320(C):288
Jiang Y, Li Z, Wang J (2017) PTrack: Enhancing the applicability of pedestrian tracking with wearables. In: IEEE international conference on distributed computing systems, pp 2193–2199
Xiang L, Tai TY, Li B, Li B (2017) Tack: learning towards contextual and ephemeral indoor localization with crowdsourcing. IEEE J Select Areas Commun PP(99):1
Liu K, Wu D, Li X (2016) Enhancing smartphone indoor localization via opportunistic sensing. In: IEEE International conference on sensing, communication, and networking, pp 1–9
Huang W, Xiong Y, Li XY, Lin H, Mao X, Yang P, Liu Y, Wang X (2015) Swadloon: Direction finding and indoor localization using acoustic signal by shaking smartphones. IEEE Trans Mob Comput 14(10):2145
Zhao Z, Wang J, Zhao X, Peng C, Guo Q, Wu B (2017) NaviLight: Indoor localization and navigation under arbitrary lights. In: INFOCOM 2017 - IEEE conference on computer communications IEEE
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Gong, J., Ren, J. & Zhang, Y. DeepNav: A scalable and plug-and-play indoor navigation system based on visual CNN. Peer-to-Peer Netw. Appl. 14, 3718–3736 (2021). https://doi.org/10.1007/s12083-021-01216-0
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DOI: https://doi.org/10.1007/s12083-021-01216-0