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Orientation constraints for Wi-Fi SLAM using signal strength gradients

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A Correction to this article was published on 27 August 2020

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Abstract

We propose the signal strength gradient (SSG) orientation constraints for simultaneous localization and mapping (SLAM) using Wi-Fi received signal strength (RSS) measurements. We show that under certain circumstances, the relative orientation between nearby trajectory segments can be recovered from the cosine similarity between their SSGs. We then show how to obtain trajectory segments and self-consistent SSGs by jointly segmenting Wi-Fi measurements and odometry. Because SSG orientation constraints inevitably contain outliers, we also evaluate the effectiveness of robust SLAM backends on the proposed constraints. Experiments show that Wi-Fi SLAM using the proposed method can correctly estimate orientations given topologically incorrect initialization on trajectories with little to no overlapping sections.

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  • 27 August 2020

    The original version of this article unfortunately missing the “Acknowledgements” section.

References

  • Agarwal, P., Tipaldi, G. D., Spinello, L., Stachniss, C., & Burgard, W. (2013). Robust map optimization using dynamic covariance scaling. In 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 6–10, 2013 (pp. 62–69). https://doi.org/10.1109/ICRA.2013.6630557.

  • Bahl, P., & Padmanabhan, V. N. (2000). Radar: An in-building RF-based user location and tracking system. In IEEE Conference on Computer Communications (INFOCOM) (pp. 775–784).

  • 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).

  • Bruno, L., & Robertson, P. (2011). Wislam: Improving footslam with WiFi. In 2011 International Conference on Indoor Positioning and Indoor Navigation (pp. 1–10).

  • Carlone, L., Aragues, R., Castellanos, J. A., & Bona, B. (2014). A fast and accurate approximation for planar pose graph optimization. The International Journal of Robotics Research, 33(7), 965–987. https://doi.org/10.1177/0278364914523689.

    Article  Google Scholar 

  • Deyle, T., Kemp, C. C., & Reynolds, M. S. (2008). Probabilistic UHF RFID tag pose estimation with multiple antennas and a multipath RF propagation model. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 22–26, 2008, Acropolis Convention Center, Nice, France (pp. 1379–1384). https://doi.org/10.1109/IROS.2008.4651170.

  • Faragher, R., Sarno, C., & Newman, M. (2012). Opportunistic radio slam for indoor navigation using smartphone sensors. In Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium (pp. 120–128).

  • Fearnhead, P. (2005). Exact bayesian curve fitting and signal segmentation. IEEE Transactions on Signal Processing, 53(6), 2160–2166. https://doi.org/10.1109/TSP.2005.847844.

    Article  MathSciNet  MATH  Google Scholar 

  • Fearnhead, P., & Liu, Z. (2007). On-line inference for multiple changepoint problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(4), 589–605. https://doi.org/10.1111/j.1467-9868.2007.00601.x.

    Article  MathSciNet  Google Scholar 

  • Ferris, B., Fox, D., & Lawrence, N. (2007). WiFi-slam using Gaussian process latent variable models. In IJCAI (pp. 2480–2485).

  • Gjengset, J., Xiong, J., McPhillips, G., & Jamieson, K. (2014). Phaser: Enabling phased array signal processing on commodity WiFi access points. In The 20th Annual International Conference on Mobile Computing and Networking, MobiCom’14, Maui, HI, USA, September 7–11, 2014 (pp. 153–164). https://doi.org/10.1145/2639108.2639139.

  • Gu, Y., Zhou, C., Wieser, A., & Zhou, Z. (2019). Trajectory estimation and crowdsourced radio map establishment from foot-mounted imus, Wi-Fi fingerprints, and gps positions. IEEE Sensors Journal, 19, 1104–1113.

    Article  Google Scholar 

  • Gutmann, J., Eade, E., Fong, P., & Munich, M. E. (2012). Vector field SLAM—Localization by learning the spatial variation of continuous signals. IEEE Transactions on Robotics, 28(3), 650–667. https://doi.org/10.1109/TRO.2011.2177691.

    Article  Google Scholar 

  • Halperin, D., Hu, W., Sheth, A., & Wetherall, D. (2011). Tool release: Gathering 802.11n traces with channel state information. ACM SIGCOMM Computer Communication Review, 41(1), 53–53. https://doi.org/10.1145/1925861.1925870.

    Article  Google Scholar 

  • Hashemifar, Z. S., Adhivarahan, C., Balakrishnan, A., & Dantu, K. (2019). Augmenting visual slam with Wi-Fi sensing for indoor applications. Autonomous Robots, 43, 2245–2260.

    Article  Google Scholar 

  • Herranz, F., Llamazares, A., Molinos, E. J., & Ocaña, M. (2014). A comparison of SLAM algorithms with range only sensors. In 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, May 31–June 7, 2014 (pp. 4606–4611). https://doi.org/10.1109/ICRA.2014.6907532.

  • Huang, J., Millman, D., Quigley, M., Stavens, D., Thrun, S., & Aggarwal, A. (2011). Efficient, generalized indoor WiFi graphslam. In ICRA (pp. 1038–1043).

  • Jiang, J., Lin, C., Lin, F., & Huang, S. (2013). ALRD: AoA localization with RSSI differences of directional antennas for wireless sensor networks. IJDSN. https://doi.org/10.1155/2013/529489.

  • Karanam, C. R., Korany, B., & Mostofi, Y. (2018). Magnitude-based angle-of-arrival estimation, localization, and target tracking. In 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) (pp. 254–265).

  • Kotaru, M., Joshi, K. R., Bharadia, D., & Katti, S. (2015). Spotfi: Decimeter level localization using WiFi. Computer Communication Review, 45(5), 269–282. https://doi.org/10.1145/2829988.2787487.

    Article  Google Scholar 

  • Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., & Burgard, W. (2011). G\({}^{\text{2}}\)o: A general framework for graph optimization. In IEEE International Conference on Robotics and Automation, ICRA 2011, Shanghai, China, 9–13 May 2011 (pp. 3607–3613). https://doi.org/10.1109/ICRA.2011.5979949.

  • Kümmerle, R., Steder, B., Dornhege, C., Ruhnke, M., Grisetti, G., Stachniss, C., et al. (2009). On measuring the accuracy of SLAM algorithms. Autonomous Robots, 27(4), 387–407. https://doi.org/10.1007/s10514-009-9155-6.

    Article  Google Scholar 

  • Latif, Y., Lerma, C. D. C., & Neira, J. (2013). Robust loop closing over time for pose graph SLAM. The International Journal of Robotics Research, 32(14), 1611–1626. https://doi.org/10.1177/0278364913498910.

    Article  Google Scholar 

  • Lu, F., & Milios, E. E. (1997). Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4(4), 333–349. https://doi.org/10.1023/A:1008854305733.

    Article  Google Scholar 

  • Menegatti, E., Zanella, A., Zilli, S., Zorzi, F., & Pagello, E. (2009). Range-only SLAM with a mobile robot and a wireless sensor networks. In 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan, May 12–17, 2009 (pp. 8–14). https://doi.org/10.1109/ROBOT.2009.5152449.

  • Mirowski, P. W., Ho, T. K., Yi, S., & MacDonald, M. (2013). SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals. In International Conference on Indoor Positioning and Indoor Navigation (pp. 1–10).

  • Olson, E., & Agarwal, P. (2013). Inference on networks of mixtures for robust robot mapping. The International Journal of Robotics Research, 32(7), 826–840. https://doi.org/10.1177/0278364913479413.

    Article  Google Scholar 

  • Olson, E., Leonard, J. J., & Teller, S. J. (2006). Fast iterative alignment of pose graphs with poor initial estimates. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, May 15–19, 2006, Orlando, Florida, USA (pp. 2262–2269). https://doi.org/10.1109/ROBOT.2006.1642040.

  • Pan, J. J., Pan, S. J., Yin, J., Ni, L. M., & Yang, Q. (2012). Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 587–600. https://doi.org/10.1109/TPAMI.2011.165.

    Article  Google Scholar 

  • Pfingsthorn, M., & Birk, A. (2016). Generalized graph SLAM: Solving local and global ambiguities through multimodal and hyperedge constraints. The International Journal of Robotics Research, 35(6), 601–630. https://doi.org/10.1177/0278364915585395.

    Article  Google Scholar 

  • Schüssel, M. (2016). Angle of arrival estimation using WiFi and smartphones. In International Conference on Indoor Positioning and Indoor Navigation (IPIN).

  • Sünderhauf, N., & Protzel, P. (2012). Towards a robust back-end for pose graph SLAM. In IEEE International Conference on Robotics and Automation, ICRA 2012, 14–18 May, 2012, St. Paul, Minnesota, USA (pp. 1254–1261). https://doi.org/10.1109/ICRA.2012.6224709.

  • Tzur, A., Amrani, O., & Wool, A. (2015). Direction finding of rogue Wi-Fi access points using an off-the-shelf MIMO-OFDM receiver. Physical Communication, 17, 149–164. https://doi.org/10.1016/j.phycom.2015.08.010.

    Article  Google Scholar 

  • Xiong, H., & Tao, D. (2017). A diversified generative latent variable model for WiFi-slam. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4–9, 2017, San Francisco, California, USA (pp. 3841–3847).

  • Xiong, J., & Jamieson, K. (2013). Arraytrack: A fine-grained indoor location system. In Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2013, Lombard, IL, USA, April 2–5, 2013 (pp. 71–84).

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Hsiao-Chieh Yen & Cheng-Fu Chou

  2. Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan

    Chieh-Chih Wang

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  1. Hsiao-Chieh Yen
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  2. Chieh-Chih Wang
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  3. Cheng-Fu Chou
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Yen, HC., Wang, CC. & Chou, CF. Orientation constraints for Wi-Fi SLAM using signal strength gradients. Auton Robot 44, 1135–1146 (2020). https://doi.org/10.1007/s10514-020-09914-z

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