A Pedestrian Navigation System Based On a Foot-Mounted IMU and Map Constraints. Dan Pierce and Dr. David Bevly

Transcription

1 A Pedestrian Navigation System Based On a Foot-Mounted IMU and Map Constraints Dan Pierce and Dr. David Bevly

2 Outline Motivation/Introduction System Description Localization Foot-mounted IMU algorithm Map constraint filter Testing description and results Navigation Human machine interface Testing description and results Future work 1

3 Motivation Enhance the mobility and independence of blind and visually impaired community Assist users in navigating large unstructured environments that they encounter in daily life 2 Parks Parking lots Airports Sports arenas Complex intersections GA VLAB

4 Challenges for Pedestrian Navigation Requires functionality in GPSstressed and GPS-denied scenarios Urban canyons, dense foliage, indoors Typical navigation devices (e.g. smart phones) are insufficient Limited GPS precision/availability Multipath effects Infrastructure-based approaches can be costly when scaling 3

5 System Description Design Requirement: Provide continued assistance for Point A to B navigation through noninvasive human-machine interface Solution: iphone application Optimal route planning within map database Smart intersection connectivity (DSRC) Vibro-tactile belt* Map aided foot-mounted IMU localization solution* 4

6 Localization: Foot-Mounted IMU A relative 3D position/orientation solution is generated by IMU mechanization An Extended Kalman Filter (EKF) is used to correct the solution with updates of zero velocity during footfall Z[m] 6 DoF Pose Step Pose X[m] Y[m] 5

7 Localization: Foot-Mounted IMU Outputs: The full 6 DoF solution is not entirely relevant for navigating the user At each step, a subset of information is extracted Change in heading (Δψ) Change in position (Δx, Δy) The step outputs can then be treated as a source of dead reckoning 6 N E ψ - = N E ψ -/0 + cos (ψ) sin (ψ) 0 sin (ψ) cos (ψ) Δx - Δy - Δψ -

8 Localization: Map Constraints Standalone dead reckoning suffers from increasing errors with distance traveled Solution is constrained using information of physical boundaries (walls, handrails, etc.) 7

9 Localization: Map Particle Filter 8 Initialization Sample state vector estimates ( particles ) are drawn from a probability distribution Time Update Each particle is processed through difference equation with synthetic noise to spread particles Estimate taken as a weighted mean of all particles

10 Localization: Map Particle Filter Map Constraint Update A line is drawn connecting the current and previous position of each particle This line is checked for intersections with any map boundaries Weighting is set to zero for particles that cross map boundaries Error drift is effectively constrained 9

11 Localization: Map Particle Filter Practical considerations Mapping requirements feasible since a map is already used for route planning Processor requirement minimal due to low state vector size (n = 3) and slow update rate (< 1 Hz) Real Time Implementation ROS environment for timing and communications OpenStreetMap for storing paths and boundaries ROS Rviz Representation 10

12 Localization: Testing Overview Six different test subjects walk a predefined path through Woltosz Engineering Research Lab Position and heading estimates compared to truth values at known waypoints along the path 11 Path consisted of two levels, a flight of stairs, and multiple extended hallways Test subjects walked with normal gait for a fair assessment of the system s performance

13 Localization: Testing Results Resulting trajectory from one of the six tests: 12

14 Localization: Testing Results System performance proved to be consistent among the variety of test subjects Localization error stayed within acceptable level of accuracy for navigation Heading Results Box Plot Position Results Box Plot Heading Error [ ] Position Error [m] Test Subject Test Subject 13

15 Navigation: Vibro-Tactile Belt Four vibratory cells located at the anti-cardinal direction of the user s waist Specific vibration patterns convey information to the user User Vibratory Cells Command Veer right/left Turn right/left Stop Pattern Front left/right Front and rear left/right All cells 14

16 Navigation: Vibro-Tactile Belt User s estimated pose is compared to series of waypoints that make up the desired path VT Belt command issued based on relative position Desired waypoint is incremented when user is within a given radius 15

17 Navigation: Testing Overview 16 Two team members from the National Federation of the Blind (NFB) traveled to Auburn to test the system Despite having very little time for familiarizing with the system, the users adapted quickly Each user was outfitted with the system, guided to the start location, and began operating the system Objective: receive qualitative feedback and suggestions from the NFB members

18 Navigation: Results 17

19 Navigation: Results 18

20 Future Work Map Particle Filter Incorporate additional information/measurements Traditional measurement types (GPS, ranging beacons, RFID, etc.) Gait constraint information Vicinity detection (e.g. ascending/descending a stairwell) Implement using different odometry source (e.g. pedometry) Navigation System Reduce form factor Reduced number of VT cells can convey same amount of information Perform all computations on handheld device Additional testing/feedback from the blind community 19

21 Thank you

22 Additional Results 60 Basement N [m] E [m]

23 Additional Results 60 First Floor N [m] E [m]

24 Additional Results 60 Second Floor N [m] E [m]

25 Additional Results 60 Third Floor N [m] E [m]