Acrux: Indoor Localization Without Strings

We present Acrux, the first indoor localization system to achieve meter level accuracy while relying exclusively on a single fix and the sensors commonly found in off-the-shelf smartphones. Acrux uses dead-reckoning, the approach that gives probably the best chance at a completely autonomous indoor localization system. Unfortunately, it has not been mastered on smartphones beyond a few dozen meters due to its inherent integration drift. As a result, all dead-reckoning based solutions in literature require periodic recalibration using input from outside -- attaching strings preventing indoor localization from becoming mainstream. While it is virtually impossible to completely eliminate integration drift, Acrux is the first solution to succeed in dead-reckoning with meter level accuracy for several hundred meters, enough to relax the requirement for periodic recalibration in most indoor scenarios. To accomplish this, Acrux replaces step-counting, the standard approach for measuring distance using sensors, with an approach that measures the speed of locomotion. Although a straightforward accurate estimation of motion speed using the erroneous sensors found on smartphones is infeasible, Acrux combines a novel approach with measurement based analysis to achieve that. Leveraging its excellent dead-reckoning capability, Acrux is shown to provide indoor localization with median error between SI0.7 meter and SI1.2 meter and 98% percentile error of SI3 meter in a dozen of scenarios in 4 different buildings -- without any recalibration.

[1]  Mo Li,et al.  Use it free: instantly knowing your phone attitude , 2014, MobiCom.

[2]  Kaigui Bian,et al.  Jigsaw: indoor floor plan reconstruction via mobile crowdsensing , 2014, MobiCom.

[3]  Agathoniki Trigoni,et al.  Robust Indoor Positioning With Lifelong Learning , 2015, IEEE Journal on Selected Areas in Communications.

[4]  Swarun Kumar,et al.  Accurate indoor localization with zero start-up cost , 2014, MobiCom.

[5]  He Wang,et al.  I am a smartphone and i can tell my user's walking direction , 2014, MobiSys.

[6]  Jie Yang,et al.  Push the limit of WiFi based localization for smartphones , 2012, Mobicom '12.

[7]  Azeem J. Khan,et al.  Barometric phone sensors: more hype than hope! , 2014, HotMobile.

[8]  Tom Minka,et al.  You are facing the Mona Lisa: spot localization using PHY layer information , 2012, MobiSys '12.

[9]  Aboelmagd Noureldin,et al.  Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation , 2010, IEEE Transactions on Intelligent Transportation Systems.

[10]  M. Denny,et al.  The Science of Navigation: From Dead Reckoning to GPS , 2012 .

[11]  Vincent Lenders,et al.  Deep Inspection of the Noise in WiFi Time-of-Flight Echo Techniques , 2015, MSWiM.

[12]  Jie Xiong,et al.  ArrayTrack: A Fine-Grained Indoor Location System , 2011, NSDI.

[13]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[14]  D. McCrady,et al.  Mobile ranging using low-accuracy clocks , 2000 .

[15]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[16]  Sasu Tarkoma,et al.  Accelerometer-based transportation mode detection on smartphones , 2013, SenSys '13.

[17]  Injong Rhee,et al.  Towards Mobile Phone Localization without War-Driving , 2010, 2010 Proceedings IEEE INFOCOM.

[18]  Dongho Kim,et al.  Bringing CUPID Indoor Positioning System to Practice , 2015, WWW.

[19]  Kyu-Han Kim,et al.  SAIL: single access point-based indoor localization , 2014, MobiSys.

[20]  Tao Gu,et al.  B-Loc: Scalable Floor Localization Using Barometer on Smartphone , 2014, 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems.

[21]  Sachin Katti,et al.  SpotFi: Decimeter Level Localization Using WiFi , 2015, SIGCOMM.

[22]  Shueng-Han Gary Chan,et al.  Contour-based Trilateration for Indoor Fingerprinting Localization , 2015, SenSys.

[23]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[24]  Stuart A. Golden,et al.  Sensor Measurements for Wi-Fi Location with Emphasis on Time-of-Arrival Ranging , 2007, IEEE Transactions on Mobile Computing.

[25]  W. Kraemer,et al.  FOOT STRIKE PATTERNS OF RUNNERS AT THE 15‐KM POINT DURING AN ELITE‐LEVEL HALF MARATHON , 2007, Journal of strength and conditioning research.