AirLoc MASS 2015

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  1. AirLoc: Mobile Robots Assisted Indoor Localization IEEE MASS 2015 Chen Qiu and Matt W. Mutka Dept. of Computer Science and Engineering Michigan State University 2.…
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  • 1. AirLoc: Mobile Robots Assisted Indoor Localization IEEE MASS 2015 Chen Qiu and Matt W. Mutka Dept. of Computer Science and Engineering Michigan State University
  • 2. Outdoor Localization Indoor Localization Ultrasound WSN RFID Smartphone GPS Location Based Services
  • 3. Smart HomeShopping Mall Cleaner Hospital
  • 4. Simultaneous localization and mapping (SLAM) TurtleBot What does the world look like? Where am I? Sensing Mapping Filtering Constraint: can Not locate people’s positions
  • 5. Everyone has a smartphone
  • 6. Dead Reckoning S1 S2 Sn Sn−1ax X Y Z ay az Smartphone Based Indoor Localization Smartphone’s Acceleration Not Equal to User’s body Acceleration Error Accumulation 0.5 degree error of orientation sensor 308m error within 1 minute Imperfection
  • 7. Conjecture: Could mobile robots assist Indoor localization ? Smartphone based indoor positioning • Dead Reckoning • Finger Printing / Radio Map • Other Inertial Sensing (Light, Sound, Barometer) Mobile Robot • Low cost (TurtleBot) • Accurate Position (Error within 0.3m) • Extra Services
  • 8. 0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 Time (seconds) Deviation Distance(meters) Confidence interval Confidence interval Deviations without calibration Deviations with calibration Receive Accurate Location Message (X,Y)& (X,Y)& (X,Y)& (X,Y)& Building Connection RSSI>Threshold Scanning Devices Sending Location Messages Preliminary Observation Sending Accurate Location Information
  • 9. Goal of System Design: Mobile Robot send more accurate location to smartphones Serving Routes Selection: First Serve the places with more people More Robots, Fast Robots: System Costs, Equipments limitation Sensing Range: Bluetooth (~10m), WiFi (Energy Concern) Potential Resolutions: Sampling Frequency: Cannot Define on Smartphone, Energy Consume
  • 10. Crowd Density Estimation Common Phenomenon: RSSI variation caused by human bodies 3. density levels for generating routes 1. collect features on each sub-grid High Density Level Normal Density Level Low Density Level 2. cluster samples Steps of Estimation: Feature 1: Num of Devices Feature 2: Bluetooth RSSI K-Means EM First Serve Higher Density
  • 11. Problem Formulation Node Edge Room Hallway Graph Indoor Map
  • 12. Robot’s Traveling Strategy Traveling Salesman Problem (TSP): • Find the best way to visit all the cities • Minimal travel time • NP-hard Problem AirLoc focuses on time cost of routes and rooms Edge Based Algorithm (EBA): • Both edges and nodes are assigned weights • Dynamic Programming to find the route with minimal travel time • Approximate solution (NP-hard Reduction)
  • 13. Edge Based Algorithm (EBA) R1(HD) R5(LD) R4(HD)R3(LD) R2(MD) 1.5min 2min 0.5min 1min LD – Low Density 0.5m in0.5m in 0.5m in 0.5m in 0.5m in 0.5m in MD – Medium Density HD – High Density – Delete after round 1 – Delete after round 2
  • 14. Parallel Function 1: Computing Serving Routes Density Levels Serving Routes Clustering Dynamic Programming RSSI Collection (X,Y)& (X,Y)& (X,Y)& (X,Y)& Moving Robot Known Map Mobile APP Parallel!Function 2: Sending Accurate Location Information Building Connection RSSI>Threshold 1. Bluetooth RSSI 2. Number of Devices Scanning Devices Sending Location Messages Overview of Single Robot Based System
  • 15. Multi-robots System Design Single robot is not enough Environment with more rooms Crowd density is dynamic More robots, better accuracy Multi-robot design strategy
  • 16. Two-robots System Design Partition the graph to two components Principle: allocate more time to higher density rooms Trade off between Distance and Density Principle: balance between the two aspects Principle: limit the time costs on the edges Density First Algorithm (DenFA) Distance First Algorithm (DisFA) Distance/Density First Algorithm (DDFA)
  • 17. 1 0 0 2 1 8 8 9 Density First Algorithm (DenFA) X axis Yaxis Merge to Low Density Area
  • 18. 1 0 0 2 1 8 8 9 Distance First Algorithm (DisFA) X axis Yaxis Merge to High Density Area
  • 19. Distance/Density First Algorithm (DDFA) Combine DisFA and DenFA Keep Connectivity in each component Use thresholds to assign weights T1 and T2 , Distance First T1 and T2 , Density First
  • 20. Low Density AreaHigh Density Area nLDA nHDA ≤10% Preemption Period: 21 % 2n % 22 % 23 % nLDA nHDA >10% Exponential growth Preemption: make robots more efficiency
  • 21. Low Density AreaHigh Density Area Preemption Period: nLDA nHDA >10% Return to Initial State 20 % Preemption: make robots more efficiency
  • 22. Extend two robots to multi-robots - Higher Crowd Density Area Unbalance Serving Tree Number of Devices AverageRSSI(%) Room i Allocate robots to HDA(P ×θ) /(θ +1) Allocate robots to LDAP − (P ×θ) / (θ +1) θ = ( ωi i=1 H ∑ ) /( ω j j=1 L ∑ ) Definition of ω P - num of robots
  • 23. Extend Two Robots to Multi-robots How robots go back to the root ? Return by the way you came Dynamic Return Approach Find node k: the smallest sum of distances between k and other rooms Arrange k as the “new” root min( Dist j=1 n ∑ i=1 n ∑ (i, j)) crowd density change, waste time
  • 24. AirLoc System Evaluation Experiment Setting •The height of the tablet is 1 meter •The speed of is 0.3m /s •0-6 volunteers carry Samsung Galaxy 4 or Google Nexus Tablet •Employ on Bluetooth Adapter to communicate •Volunteers in the experimental environment walk freely Metrics Deviation Distance: Euclidean Distance (meter) L(x) = − P(xi )log2 (P(xi )) i=1 m ∑Location Entropy:
  • 25. Data Collection in Indoor Building Data collected in a room Data collected in a hallway - Robot Calibration - Smartphone Position Data samples on the map
  • 26. 1 2 3 1 4 5 7 8 9 10 11 12 13 14 15 16 17 2 3 4 5 8 7 6 10 1112 15 14 13 16 18 19 21 20 22 24 23 25 17 Ground Truth Estimated Trace 20 19 18 23 22 21 2524 R1 R1 R2 R1 R3 - Send Location Message - Sequence Num of RobotR# - Calibrate Deviation 1100’s Floor Plan R3 Cloud Server 9 Single Trace Study in Real Floorpan • Calibrated by robots, the errors are reduced • Dead reckoning yields obvious deviations • Mobile robots update the crowd density in a cloud Conclusion:
  • 27. T Slot S Slot R Slot T Slot … … Update Density Levels Form Final Serving AreaDivide Groups Loop OPOS (One Period One Sample): T Slot OPMS (One Period Multiple Sample): T Slot & S Slot Static crowd density: initial state Crowd Density Updating Conclusion: • Multi-robots update crowd density continuously • Real Time crowd density improves the localization results Duty cycle of AirLoc Compare different density information 2 4 6 8 200 300 400 500 600 Number of rounds Numberof deviationgrids OPMS crowd density Static crowd density OPOS crowd density
  • 28. 0 2 4 6 8 10 12 0 0.2 0.4 0.6 Number of rounds LocationEntropy BalanceTree−Dynamic Return BalanceTree−Static Return UnblanceTree−Static Return UnblanceTree−Dynamic Return 1 2 3 4 5 6 0 50 100 Number of smartphones AverageRSS(%) Low Density Level High Density Level Normal Density Level Centroids of clusters 1 2 3 4 5 6 100 200 300 400 500 Average degree of nodes Numberof deviationgrids 32 Robots 8 Robots EBA Evaluation Results Cluster different density levels for all the rooms Unbalanced Tree outperforms Balance Tree Dynamic Return enhances AirLoc More robots provide more accuracy
  • 29. Summary • Mobile robots interact with smartphones to send accurate location information • AirLoc organizes multi-robots for improving the smartphones’ positioning information • Single robot adopts Edge Based Algorithm (EBA) to generate the optimized serving route • AirLoc updates the crowd density levels continuously Distance/Density First Algorithm Dynamic Return PreemptionUnbalanced Serving Tree
  • 30. Thank you ! Questions ?
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