Autonomous Robots

, Volume 43, Issue 8, pp 2245–2260 | Cite as

Augmenting visual SLAM with Wi-Fi sensing for indoor applications

  • Zakieh S. HashemifarEmail author
  • Charuvahan Adhivarahan
  • Anand Balakrishnan
  • Karthik Dantu


Recent trends have accelerated the development of spatial applications on mobile devices and robots. These include navigation, augmented reality, human–robot interaction, and others. A key enabling technology for such applications is the understanding of the device’s location and the map of the surrounding environment. This generic problem, referred to as Simultaneous Localization and Mapping (SLAM), is an extensively researched topic in robotics. However, visual SLAM algorithms face several challenges including perceptual aliasing and high computational cost. These challenges affect the accuracy, efficiency, and viability of visual SLAM algorithms, especially for long-term SLAM, and their use in resource-constrained mobile devices. A parallel trend is the ubiquity of Wi-Fi routers for quick Internet access in most urban environments. Most robots and mobile devices are equipped with a Wi-Fi radio as well. We propose a method to utilize Wi-Fi received signal strength to alleviate the challenges faced by visual SLAM algorithms. To demonstrate the utility of this idea, this work makes the following contributions: (i) We propose a generic way to integrate Wi-Fi sensing into visual SLAM algorithms, (ii) We integrate such sensing into three well-known SLAM algorithms, (iii) Using four distinct datasets, we demonstrate the performance of such augmentation in comparison to the original visual algorithms and (iv) We compare our work to Wi-Fi augmented FABMAP algorithm. Overall, we show that our approach can improve the accuracy of visual SLAM algorithms by 11% on average and reduce computation time on average by 15% to 25%.


Visual SLAM Wi-Fi sensing Perceptual aliasing 



National Science Foundation (Grant Nos. RI 1514395, CNS 1846320).


  1. Belter, D., Nowicki, M., & Skrzypczyński, P. (2016). Improving accuracy of feature-based rgb-d slam by modeling spatial uncertainty of point features. In 2016 IEEE international conference on robotics and automation (ICRA), IEEE (pp. 1279–1284)Google Scholar
  2. Berkvens, R., Jacobson, A., Milford, M., Peremans, H., & Weyn, M. (2014). Biologically inspired slam using wi-fi. In 2014 IEEE/RSJ international conference on intelligent robots and systems (pp. 1804–1811).Google Scholar
  3. Biswas, J., & Veloso, M. (2010). Wifi localization and navigation for autonomous indoor mobile robots. In 2010 IEEE international conference on robotics and automation (pp. 4379–4384).Google Scholar
  4. Clark, R., Wang, S., Wen, H., Trigoni, N., & Markham, A. (2016). Increasing the efficiency of 6-dof visual localization using multi-modal sensory data. In 2016 IEEE-RAS 16th international conference on humanoid robots (humanoids), IEEE (pp. 973–980).Google Scholar
  5. Codd-Downey, R. & Jenkin, M. (2017). On the utility of additional sensors in aquatic simultaneous localization and mapping. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 5163–5168).Google Scholar
  6. Cummins, M., & Newman, P. (2008). Accelerated appearance-only slam. In 2008 IEEE international conference on robotics and automation (pp. 1828–1833).Google Scholar
  7. Dong, J., Xiao, Y., Noreikis, M., Ou, Z., & Ylä-Jääski, A. (2015). imoon: Using smartphones for image-based indoor navigation. In Proceedings of the 13th ACM conference on embedded networked sensor systems, ACM (pp. 85–97).Google Scholar
  8. Engelhard, N., Endres, F., Hess, J., Sturm, J., & Burgard, W. (2011). Real-time 3D visual SLAM with a hand-held camera. In Proceedings of the RGB-D workshop on 3D perception in robotics at the European robotics forum. Vasteras, Sweden.Google Scholar
  9. García, S., López, M. E., Barea, R., Bergasa, L. M., Gómez, A., & Molinos, E. J. (2016). Indoor slam for micro aerial vehicles control using monocular camera and sensor fusion. In 2016 international conference on autonomous robot systems and competitions (ICARSC) (pp. 205–210).Google Scholar
  10. Glover, A., Maddern, W., Warren, M., Reid, S., Milford, M., & Wyeth, G. (2012). Openfabmap: An open source toolbox for appearance-based loop closure detection. In 2012 IEEE international conference on robotics and automation (ICRA), IEEE (pp. 4730–4735).Google Scholar
  11. Heshmat, M., Abdellatif, M., & Abbas, H. (2013). Improving visual slam accuracy through deliberate camera oscillations. In 2013 IEEE international symposium on robotic and sensors environments (ROSE) (pp. 154–159).Google Scholar
  12. Hess, W., Kohler, D., Rapp, H., & Andor, D. (2016). Real-time loop closure in 2d lidar slam. In 2016 IEEE international conference on robotics and automation (ICRA), IEEE (pp. 1271–1278).Google Scholar
  13. Huang, J., Millman, D., Quigley, M., Stavens, D., Thrun, S., & Aggarwal, A. (2011). Efficient, generalized indoor wifi graphslam. In 2011 IEEE international conference on robotics and automation (pp. 1038–1043).Google Scholar
  14. Ito, S., Endres, F., Kuderer, M., Tipaldi, G. D., Stachniss, C., & Burgard, W. (2014). W-rgb-d: Floor-plan-based indoor global localization using a depth camera and wifi. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 417–422).Google Scholar
  15. Jacobson, A., Chen, Z., Rallabandi, V. R., Milford, M., & (2015). Multi-scale place recognition with multi-scale sensing. In Australasian conference on robotics and automation (ACRA 2015). Canberra: Australasian Robotics and Automation Association: A.C.T.Google Scholar
  16. Jung, J., Oh, T., & Myung, H. (2015). Magnetic field constraints and sequence-based matching for indoor pose graph slam. Robotics and Autonomous Systems, 70, 92–105.CrossRefGoogle Scholar
  17. Kabsch, W. (1976). A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography, 32(5), 922–923.CrossRefGoogle Scholar
  18. Karanam, C. R., Korany, B., & Mostofi, Y. (2018). Magnitude-based angle-of-arrival estimation, localization, and target tracking. In Proceedings of the 17th ACM/IEEE international conference on information processing in sensor networks (pp. 254–265). IEEE Press.Google Scholar
  19. Kejriwal, N., Kumar, S., & Shibata, T. (2016). High performance loop closure detection using bag of word pairs. Robotics and Autonomous Systems, 77, 55–65.CrossRefGoogle Scholar
  20. Kotaru, M., Joshi, K., Bharadia, D., & Katti, S. (2015). Spotfi: Decimeter level localization using wifi. In Proceedings of the 2015 ACM conference on special interest group on data communication, SIGCOMM ’15 (pp. 269–282). New York: ACM.Google Scholar
  21. Kumar, S. S., Gil, S., Katabi, D., & Rus, D. (2018). Indoor localization of a multi-antenna receiver. US Patent 9,885,774.Google Scholar
  22. Kuo, Y.-S., Pannuto, P., Hsiao, K.-J., & Dutta, P. (2014). Luxapose: Indoor positioning with mobile phones and visible light. In Proceedings of the 20th annual international conference on mobile computing and networking, MobiCom ’14 (pp. 447–458). New York: ACM.Google Scholar
  23. Labbe, M., & Michaud, F. (2014). Online global loop closure detection for large-scale multi-session graph-based slam. In 2014 IEEE/RSJ international conference on intelligent robots and systems (IROS 2014), IEEE (pp. 2661–2666).Google Scholar
  24. Labbé, M., & Michaud, F. (2011). Memory management for real-time appearance-based loop closure detection. In 2011 IEEE/RSJ international conference on intelligent robots and systems (pp. 1271–1276).Google Scholar
  25. Labbé, M., & Michaud, F. (2013). Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Transactions on Robotics, 29(3), 734–745.CrossRefGoogle Scholar
  26. Lu, C. X., Li, Y., Zhao, P., Chen, C., Xie, L., Wen, H., Tan, R., & Trigoni, N. (2018). Simultaneous localization and mapping with power network electromagnetic field. In Proceedings of the 24th annual international conference on mobile computing and networking, MobiCom ’18 (pp. 607–622). New York: ACM.Google Scholar
  27. Luo, C., Hong, H., & Chan, M. C. (2014). Piloc: A self-calibrating participatory indoor localization system. In Proceedings of the 13th international symposium on information processing in sensor networks, IPSN ’14 (pp. 143–154). Piscataway: IEEE Press.Google Scholar
  28. Mur-Artal, R., & Tardós, J. D. (2017). ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Transactions on Robotics, 33(5), 1255–1262.CrossRefGoogle Scholar
  29. Nguyen, D. V., Recalde, M. E. V., & Nashashibi, F. (2016). Low speed vehicle localization using wifi fingerprinting. 2016 14th international conference on control, automation, robotics and vision (ICARCV) (pp. 1–5).Google Scholar
  30. Nowakowski, M., Joly, C., Dalibard, S., Garcia, N., & Moutarde, F. (2017). Topological localization using wi-fi and vision merged into fabmap framework. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3339–3344).Google Scholar
  31. Nowicki, M. (2014). Wifi-guided visual loop closure for indoor navigation using mobile devices. Journal of Automation Mobile Robotics and Intelligent Systems, 8(3), 10–18.CrossRefGoogle Scholar
  32. Quattoni, A., & Torralba, A. (2009). Recognizing indoor scenes. In IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009., IEEE (pp. 413–420).Google Scholar
  33. Quigley, M., Stavens, D., Coates, A., & Thrun, S. (2010). Sub-meter indoor localization in unmodified environments with inexpensive sensors. In 2010 IEEE/RSJ international conference on intelligent robots and systems (pp. 2039–2046).Google Scholar
  34. Soltanaghaei, E., Kalyanaraman, A., & Whitehouse, K. (2018). Multipath triangulation: Decimeter-level wifi localization and orientation with a single unaided receiver. In Proceedings of the 16th annual international conference on mobile systems, applications, and services, MobiSys ’18 (pp. 376–388). New York: ACM.Google Scholar
  35. Wang, S., Wen, H., Clark, R., & Trigoni, N. (2016). Keyframe based large-scale indoor localisation using geomagnetic field and motion pattern. In 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1910–1917).Google Scholar
  36. Xia, Y., Li, J., Qi, L., & Fan, H. (2016). Loop closure detection for visual slam using pcanet features. In 2016 international joint conference on neural networks (IJCNN) (pp. 2274–2281).Google Scholar
  37. Yang, S., Maturana, D., & Scherer, S. (2016). Real-time 3d scene layout from a single image using convolutional neural networks. In IEEE international conference on robotics and automation (ICRA), 2016, IEEE.Google Scholar
  38. Yang, S. W., Yang, S. X., & Yang, L. (2014). Method of improving wifi slam based on spatial and temporal coherence. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 1991–1996).Google Scholar
  39. 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, Mobicom ’12 (pp. 269–280). New York: ACM.Google Scholar
  40. Zhang, C., & Zhang, X. (2016). Litell: Robust indoor localization using unmodified light fixtures. In Proceedings of the 22Nd annual international conference on mobile computing and networking, MobiCom ’16 (pp. 230–242). New York: ACM.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentUniversity at BuffaloBuffaloUSA
  2. 2.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA

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