Indoor navigation using smartphones. Chris Hide IESSG, University of Nottingham, UK

Transcription

1 Indoor navigation using smartphones Chris Hide IESSG, University of Nottingham, UK

2 Overview Smartphones Available sensors Current positioning methods Positioning research at IESSG 1. Wi-Fi fingerprinting 2. Inertial navigation 3. Video+inertial+GPS navigation 4. Other research Conclusions

3 Smartphone sensors GPS Microphone Wi-Fi Ambient light sensor 3G/GPRS 3-axis accelerometer Bluetooth Proximity sensor 3-axis gyro FM radio 3-axis magnetometer Camera

4 Current smartphone positioning

5 Current positioning methods GPS Microphone Wi-Fi Ambient light sensor 3G/GPRS 3-axis accelerometer Bluetooth Proximity sensor 3-axis gyro FM radio 3-axis magnetometer Camera

6 Current positioning methods Cell-ID Wi-Fi GPS 200m

7 1. Wi-Fi fingerprinting

8 Wi-Fi fingerprinting GPS Microphone Wi-Fi Ambient light sensor 3G/GPRS 3-axis accelerometer Bluetooth Proximity sensor 3-axis gyro FM radio 3-axis magnetometer Camera

9 Wi-Fi fingerprinting Measure signal strengths to all Access Points in view Match measured signal strengths to database Requires database of: Location signal strengths to all Access Points (APs) in view Signal strengths 1. Position, ID1, SS1, ID2, SS2, ID3, SS3, Position, ID1, SS1, ID2, SS2, ID3, SS3, Position, ID1, SS1, ID2, SS2, ID3, SS3, Position, ID1, SS1, ID2, SS2, ID3, SS3, Position

10 Data collection Data collected on ground floor of Nottingham Geospatial Building HP laptop with Wi-Fi Netstumbler for Wi-Fi data collection Foot mounted IMU Microstrain 3DM-GX3 USB communication and power Outputs NMEA data Basic Wi-Fi fingerprinting software developed at IESSG

11 Signal strength for one AP Signal strength to AP 00:23:33:16:3C:90 >-40dB -40 to -50dB -50 to -60dB -60 to -70dB -70 to -80dB < -80dB

12 Basic Wi-Fi fingerprinting

13 Wi-Fi fingerprinting Works better indoors where walls/ceilings/furniture will attenuate signals the most i.e. Accuracy comes from signal strength varying spatially Advanced algorithms Particle filtering How do we build databases? Skyhook use fleet of vehicles with GPS (tribe sourcing) Google use crowd sourcing(?) But what about inside where GPS isn t working? Slow database generation using building plans Scalability? How do we keep the database up-to-date?

14 2. Inertial navigation

15 Inertial navigation GPS Microphone Wi-Fi Ambient light sensor 3G/GPRS 3-axis accelerometer Bluetooth Proximity sensor 3-axis gyro FM radio 3-axis magnetometer Camera

16 Inertial Navigation 3 gyros and 3 accelerometers Orientation from integrating gyros Displacement from rotating measurements to Earth frame (using gyros), removing gravity and double integrating accelerometers Not very accurate! MEMS getting better Cheaper (higher volumes e.g. Wii, smartphones) Better manufacturing Calibration Successful results usually from Good sensors Integration with GPS, magnetometers, zero velocity, Step detection algorithms

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18 Inertial Navigation Time (s) Horiz error (m)

19 3. Computer Vision + Inertial + GPS

20 Computer Vision + Inertial Navigation GPS Microphone Wi-Fi Ambient light sensor 3G/GPRS 3-axis accelerometer Bluetooth Proximity sensor 3-axis gyro FM radio 3-axis magnetometer Camera

21 Concept: Video Aided Inertial Successive images used to compute direction camera is moving Used to correct IMU drift

22 Computer Vision Ground plane Homography algorithm Single camera looking at plane Compute rotation and translation between images t 1

23 Examples... Computer Vision Blue (inlier correspondences) Red (outlier correspondences)

24 Integration INS corrections IMU Rotation, Acceleration INS Position, Velocity, Attitude Kalman filter Ranges, Ephemeris Position, Velocity GPS PVT computation Camera Image Computer Vision Translation

25 Experiment GPS Power

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28 Position accuracy Time (s) Horiz error (m)

29 GPS+IMU+Vision Advantages Good position accuracy Makes use of sensors already on smartphones Handheld Works with or without GPS Disadvantages Needs to be initialised e.g. with GPS Not tested with real smartphones (yet) Problems in low light conditions Computationally expensive

30 4. Other research

31 Other research GPS Microphone Wi-Fi Ambient light sensor 3G/GPRS 3-axis accelerometer Bluetooth Proximity sensor 3-axis gyro FM radio 3-axis magnetometer Maps Camera

32 Magnetometers Other research Total magnetic field varies spatially Image matching (image bag-of-words) Build database of images and locations (like Wi-Fi) Search for an image match to get location Map matching Already used for inertial and Wi-Fi (particle filtering) Walls and doors constrain user motion Direction of travel

33 Conclusions Identified some promising technologies for navigation GPS, Wi-Fi, gyros, accelerometers, magnetometers, cameras, maps sensors already available on smartphones (although not necessarily that accurate) non-dedicated infrastructure positioning e.g. Wi-Fi, images, magnetic field what else? Other sensors? needs a strong case to appear on a smartphone e.g. NFC Integration solution will comprise of several technologies? use inertial navigation to combine together?

34 Chris Hide IESSG, University of Nottingham, UK