This paper explores how inertial Pedestrian Dead-Reckoning (PDR) location systems can be improved with the use of a light sensor to measure the illumination gradients created when a person walks under ceiling-mounted unmodified indoor lights. The process of updating the inertial PDR estimates with the information provided by light detections is a new concept that we have named Light-matching (LM). The displacement and orientation change of a person obtained by inertial PDR is used by the LM method to accurately propagate the location hypothesis, and vice versa; the LM approach benefits the PDR approach by obtaining an absolute localization and reducing the PDR-alone drift. Even from an initially unknown location and orientation, whenever the person passes below a switched-on light spot, the location likelihood is iteratively updated until it potentially converges to a unimodal probability density function. The time to converge to a unimodal position hypothesis depends on the number of lights detected and the asymmetries/irregularities of the spatial distribution of lights. The proposed LM method does not require any intensity illumination calibration, just the pre-storage of the position and size of all lights in a building, irrespective of their current on/off state. This paper presents a detailed description of the light-matching concept, the implementation details of the LM-assisted PDR fusion scheme using a particle filter, and several simulated and experimental tests, using a light sensor-equipped Galaxy S3 smartphone and an external foot-mounted inertial sensor. The evaluation includes the LM-assisted PDR approach as well as the fusion with other signals of opportunity (WiFi, RFID, Magnetometers or Map-matching) in order to compare their contribution in obtaining high accuracy indoor localization. The integrated solution achieves a localization error lower than 1 m in most of the cases.
References
[1]
Mautz, R. Indoor Positioning Technologies. Ph.D. Thesis, Institute of Geodesy and Photogrammetry, Department of Civil, Environmental and Geomatic Engineering,ETH Zurich, Switzerland, 2012.
[2]
Gu, Y.; Lo, A.; Niemegeers, I. A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 2009, 11, 13–32.
[3]
Hightower, J.; Borriello, G. Location systems for ubiquitous computing. Computer 2001, 34, 57–66.
[4]
Jiménez, A.; Seco, F.; Prieto, J.; Guevara, J. A Comparison Of Pedestrian Dead-Reckoning Algorithms Using a Low-Cost MEMS IMU. Proceedings of the 2009 IEEE International Symposium on Intelligent Signal Processing, Budapest, Hungary, 26–28 August 2009; pp. 37–42.
[5]
Mautz, R. The Challenges of Indoor Environments and Specification on Some Alternative Positioning Systems. Proceedings of the Positioning, Navigation and Communication WPNC′09, Hannover, Germany, 19 March 2009; pp. 29–36.
[6]
Miller, L. Indoor Navigation for First Responders: A Feasibility Study. Technical Report; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2006.
[7]
Foxlin, E. Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graph. Appl. 2005, 25, 38–46.
[8]
Feliz, R.; Zalama, E.; García-Bermejo, J. Pedestrian tracking using inertial sensors. J. Phys. Agents 2009, 3, 35–43.
[9]
Jiménez, A.; Seco, F.; Prieto, J.; Guevara, J. Indoor Pedestrian Navigation Using an INS/EKF Framework for Yaw Drift Reduction and a Foot-Mounted IMU. Proceedings of the WPNC 2010: 7th Workshop on Positioning, Navigation and Communication, Dresden, Germany, 11–12 March 2010; Volume 10, pp. 135–143.
[10]
Harle, R. A Survey of indoor inertial positioning systems for pedestrians. IEEE Commun. Surv. Tutor. 2013, 15, 1281–1293.
[11]
Jiménez, A.; Granja, F.S.; Honorato, J.C.P.; Guevara Rosas, J. Accurate pedestrian indoor navigation by tightly coupling a foot-mounted IMU and RFID measurements. IEEE Trans. Instrum. Meas. 2012, 61, 178–189.
[12]
Woodman, O. Introduction to Inertial Navigation. Technical Report UCAM-CL-TR-696; University of Cambridge: Cambridge, UK, 2007.
[13]
Merry, L.A.; Faragher, R.M.; Scheding, S. Comparison of Opportunistic Signals for Localisation. Proceedings of the 7th IFAC Symposium on Intelligent Autonomous Vehicles, Lecce, Italy, 6–8 September 2010; pp. 109–114.
[14]
Dammann, A.; Sand, S.; Raulefs, R. Signals of Opportunity in Mobile Radio Positioning. Proceedings of the 2012 Proceedings of the European Signal Processing Conference, EUSIPCO 2012, Bucharest, Romania, 27–31 August 2012; pp. 549–553.
[15]
Golding, A.; Lesh, N. Indoor Navigation Using a Diverse Set of Cheap, Wearable Sensors. Proceedings of the Third International Symposium on Wearable Computers, San Francisco, CA, USA, 18–19 October 1999; pp. 29–36.
[16]
Golding, A. Indoor Navigation with Wearable Passive Sensors U.S. Patent 6,323,807 B1, 2001.
[17]
Ravi, N.; Iftode, L. Fiatlux: Fingerprinting Rooms Using Light Intensity. Proceedings of the Fifth International Conference on Pervasive Computing, Toronto, ON, Canada, 13–16 September 2009.
[18]
Azizyan, M.; Choudhury, R.R. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting. Proceedings of the MobiCom'09, Beijing, China, 20–25 September 2009; pp. 1–12.
[19]
Randall, J.; Amft, O.; Troster, G. Towards LuxTrace: Using Solar Cells to Measure Distance Indoors. Proceedings of the LOCA 2005: International Conference on Location and Context Awareness, Oberpfaffenhofen, Germany, 12–13 May 2005; pp. 40–51.
[20]
Amft, O.; Randall, J.; Troster, G. Towards LuxTrace: Using Solar Cells to Support Human Position Tracking. Proceedings of the IFAWC 2005: Second International Forum on Applied Wearable Computing, Zürich, Switzerland, 17–18 March 2005; pp. 63–78.
[21]
Randall, J.; Amft, O.; Burri, M. LuxTrace—Indoor positioning using building illumination. Pers. Ubiquitous Comput. 2006, 11, 417–428.
[22]
Launay, F.; Ohya, A.; Yuta, S. A Corridors Lights Based Navigation System Including Path Definition Using a Topologically Corrected Map for Indoor Mobile Robots. Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Washington, DC, USA, 11–15 May 2002; Volume 4, pp. 3918–3923.
[23]
Nakajima, M.; Haruyama, S. New indoor navigation system for visually impaired people using visible light communication. EURASIP J. Wirel. Commun. Netw. 2013, 2013, doi:10.1186/1687-1499-2013-37.
[24]
Ambur, M. Indoor positioning and navigation using light sensor. Int. J. Res. Comput. Commun. Technol. 2013, 2, 20–23.
[25]
Jiménez, A.R.; Zampella, F.; Seco, F. Light-Matching: A new Signal of Opportunity for Pedestrian Indoor Navigation. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Montbeliard-Belfort, France, 28–31 October 2013; pp. 777–786.
[26]
Abdulrahim, K.; Hide, C.; Moore, T.; Hill, C. Aiding MEMS IMU with Building Heading for Indoor Pedestrian Navigation. Proceedings of the IEEE Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), Kirkkonummi, Finland, 14–15 October 2010; pp. 1–6.
[27]
Borenstein, J.; Ojeda, L. Heuristic drift elimination for personnel tracking systems. J. Navig. 2010, 63, 591–606.
[28]
Calmer, J. Robust Heading Estimation Indoors. Technical Report LiTH-ISY-R-3060; Department of Electrical Engineering: Link？ping, Sweden, 2013.
[29]
Jiménez, A.; Seco, F.; Zampella, F.; Prieto, J.; Guevara, J. Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings. J. Locat. Based Serv. 2012, 6, 186–210.
[30]
Afzal, M.H.; Renaudin, V.; Lachapelle, G. Magnetic Field based Heading Estimation for Pedestrian Navigation Environments. Proceedings of the 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Guimaraes, Portugal, 21–23 September 2011.
[31]
Zampella, F.; Khider, M. Unscented Kalman Filter and Magnetic Angular Rate Update (Maru) for an Improved Pedestrian Dead-Reckoning. Proceedings of the 2012 IEEE/ION Position Location and Navigation Symposium (PLANS), Myrtle Beach, SC, USA, 23–26 April 2012.
[32]
Krach, B.; Robertson, P. Cascaded Estimation Architecture for Integration of Foot-Mounted Inertial Sensors. Proceedings of the 2008 IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, USA, 5–8 May 2008; pp. 112–119.
[33]
Walder, U.; Bernoulli, T. Context-Adaptive Algorithms to Improve Indoor Positioning with Inertial Sensors. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland, 15–17 September 2010; pp. 965–970.
[34]
Khider, M.; Kaiser, S.; Robertson, P.; Angermann, M. The Effect of Maps-Enhanced Novel Movement Models on Pedestrian Navigation Performance. Proceedings of The 12th Annual European Navigation Conference (ENC 2008), Toulouse, France, 22–28 April 2008.
[35]
Davidson, P.; Collin, J.; Takala, J. Application of Particle Filters for Indoor Positioning Using Floor Plans. Proceedings of the IEEE Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), Kirkkonummi, Finland, 14–15 October 2010; pp. 1–4.
[36]
Arulampalam, M.S.; Maskell, S.; Gordon, N.; Clapp, T. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Process. 2002, 50, 174–188.
[37]
Zampella, F.; Bahillo, A.; Prieto, J.; Jimenez, A.; Seco, F. Indoor positioning using pedestrian dead reckoning and RSS/TOF measurements. Sens. Actuator A Phys. 2013. in press.
[38]
Mezentsev, O.; Collin, J. Pedestrian dead reckoning—A solution to navigation in GPS signal degraded areas? Geomatica 2005, 59, 175–182.
[39]
El-Sheimy, N.; Hou, H.; Niu, X. Analysis and modeling of inertial sensors using allan variance. IEEE Trans. strum. Meas. 2008, 57, 140–149.
[40]
De Agostino, M.; Manzino, A.M.; Piras, M. Performance Comparison of Different MEMS-Based IMUs. Proceedings of the Position Location And Navigation Symposium, Indian Wells, CA, USA, 4–6 May 2010; p. p. 15.
[41]
Wan, S.; Foxlin, E. Improved Pedestrian Navigation Based on Drift-Reduced MEMS IMU Chip. Proceedings of the 2010 International Technical Meeting of the The Institute of Navigation, San Diego, CA, USA, 25–27 January 2010; pp. 220–229.
