crowdinside automatic construction of indoor floor plans n.
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  1. CROWDINSIDE: AUTOMATIC CONSTRUCTION OF INDOOR FLOOR PLANS By Team 11 Ayesha Begum MounikaKolluri SravaniDhanekula

  2. OUTLINE • Motivation • Problem Definition • Related Work • Contribution • Method • Key Concepts • Validation/Results • Conclusion • References 1/41

  3. MOTIVATION • GPS is considered as ubiquitous outdoor localization system. • It cannot be used in indoor environments due to the requirement of maintaining a line of sight with satellites. • No corresponding system for indoor localization. • Reason? 2/41

  4. REASON 1. No technology for worldwide indoor positioning - WiFi? • Requires training. 2. No worldwide indoor floor plan database - Not available - As in developing countries - Available - No one is willing to take the effort to upload/update them (Google and Bing Maps). - Privacy concern. 3/41

  5. PROBLEM DEFINITION To automatically construct floor plans virtually in buildings around the world based on crowdsourcing. 4/41

  6. SOLUTION • Approach is to leverage cellphones. • Users motion inside buildings reflect the building structure. 5/41

  7. GOAL 6/41

  8. RELATED WORK • Previous work in dead reckoning used IMUs - Inertial sensors in mobile phones are cheaper -> noisier • Step counting techniques required specific placement for the IMUs. - E.g. mounted on foot, on head • Step size constant. • SLAM (Simultaneous Localization and Mapping) cannot be implemented in commodity mobile phones. • Maps generated by SmartSLAMdescribes only the corridor layout of the building. No information about the number of rooms, shapes e.t.c 7/41

  9. CONTRIBUTION • Presents Crowdinside system architecture. • Techniques for estimating points of interest in the environment. • A novel technique for constructing accurate indoor user traces. • Classification techniques to separate corridors from rooms. • Identifies rooms shapes using computational geometry techniques. • Implements the system on different android phones. • Finally, evaluates the system in a campus building and a mall. 8/41

  10. METHOD • Method proposed is CrowdInsidewhich is based on a crowdsourcing approach. • Collects sensor data from different users moving naturally inside the buildings. • A large number of motion traces can provide an adequate description of the building’s layout. 9/41

  11. KEY CONCEPTS • Data Collection Module - Collects raw sensor measurements from users phones • Motion Traces Generator • Uses dead reckoning to track users motion • Statistical approach to find accurate starting point. • Anchors Extraction Module • Identify pols inside the building (stairs, elev., esc) • Employ anchors to reset the dead reckoning Error • Floor Estimation Module - Fuse the collected traces together into a floorplan 10/41

  12. Data Collection Module • Collected data are the measurements from • Accelerometers • Magnetometers • Gyroscopes • Also collects data from WiFireceived signal strengths from access points. • GPS is queried with low duty cycle. 11/41

  13. Traces Generation Module • Dead-reckoning based approach • Xk, Yk is current location • Xk-1, Yk-1 is previous location • S is distance traveled • Θ is direction of motion 12/41

  14. Traces Generation Module (Contd..) • Distance is two times integration of acceleration with respect to time. • In addition, there is component of gravity of earth. • So the errors grow cubically with time. • Pedometer based approach reduces error linear to time. 13/41

  15. Anchor based error resetting • Inaccuracy in tracing are due to two reasons. They are, • Inaccuracy in estimating starting point • Displacement with time • Anchor points are the points in the environment with unique sensor signatures used to reset trace error when the user hits one. 14/41

  16. Anchor Points Versus Dead Reckoning Time (sec) 15/41

  17. Anchor based approach • Two classes to identify anchor points, - Based on GPS sensor • Building entrances and windows - Based on inertial sensors • Stairs, elevators, escalators • Used for error resetting and higher semantic maps. 16/41

  18. GPS based anchor points • Building Entrance - Loss of GPS signal • Low duty cycle 17/41

  19. Inertial based Anchor points • Differentiates 5 different categories • Elevator • Escalator • Stairs • Walking • Stationary 18/41

  20. Inertial based anchor points (Contd..) • ELEVATOR • It has a unique and repeatable pattern • Finite State Machine 19/41

  21. Inertial based anchor points (Contd…) • ESCALATOR • Constant Velocity • Distinguished from stationary by variance of magnetic field effecting the smart phone 20/41

  22. Variance of acceleration 21/41

  23. Inertial based anchor points (Contd..) • STAIRS - Value of correlation between Z and Y axes acceleration is used to separate stairs and walking 22/41

  24. Inertial based anchor points (Contd..) • Stairs Up and Down 23/41

  25. Complete classification tree 24/41

  26. Floor Plan Estimation • Objective is to determine, • Overall floor plan shape. • Rooms/corridor shape. • Overall floor plan shape • Traces are collected from different users. • Point cloud is obtained from traces where each point represents the user step. • α – shape is used to capture the building shape. 25/41

  27. Floor Plan Estimation (Contd..) 26/41

  28. Floor Plan Estimation (Contd..) • Detailed floor plan -Traces segmentation and filtering 27/41

  29. Floor Plan Estimation (Contd..) • Segments classification • Segments are classified either as corridors or rooms. • Classification is done based on features • average time spent per step • segment length • neighbor trace density 28/41

  30. Separation of adjacent rooms • Segments clustering - Distance between segments mid points - Nearby rooms cannot be separated by spatial distances. 29/41

  31. Floor Plan Estimation (Contd..) • Measured WiFi signals similarity - Used to separate adjacent rooms 30/41

  32. Floor Plan Estimation (Contd..) • Estimating room doors: • Detect the intersection points of corridor and room. • DBSCAN clustering is used in order to get the possible location for a door. • Centroid of each of these cluster’s is the estimated door location. 31/41

  33. Floor Plan Estimation (Contd..) Intersection Points Estimated door locations 32/41

  34. VALIDATION/RESULTS • Experiments were performed on different android phones. • Experiments were done in two test beds: • a shopping mall with plenty of stairs/elevators/ escalators • a building in campus with an approximately 448m2area. • The first test bed is used to evaluate the accuracy of trace generation and anchor-based error resetting. • The second test bed is used for evaluating the floor plan construction as we already have access to most of the rooms 33/41

  35. Anchor Points Estimation Accuracy 34/41

  36. Anchor Extraction Module 35/41

  37. Inertial based Anchor Points 36/41

  38. Performance of anchor detection 37/41

  39. Floor Plan Estimation Accuracy 38/41

  40. Demo Of Crowd Inside 39/41

  41. CONCLUSION • Crowd Inside is completely autonomous and depends only on the data collected from users moving naturally inside the buildings. • Based on the accurate user traces, different approaches are described for detecting both the floor plan layout along with rooms, corridors, and doors. • Currently, Crowd Inside is being expanded in multiple directions including inferring higher level semantic information, such as rooms types and owners, energy-efficiency aspects, user incentivesetc. 40/41

  42. REFERENCES • M. Alzantot and M. Youssef. UPTIME: Ubiquitous pedestrian tracking using mobile phones. In IEEE Wireless Communications and Networking Conference (WCNC 2012).IEEE. • R. Azuma. Tracking requirements for augmented reality. Communications of the ACM, 36(7), July 1997. • E. S. Bhasker, S. W. Brown, and W. G. Griswold. Employing user feedback for fast, accurate, low-maintenance geo locationing. PERCOM ’04, 2004. • M. Buchin, A. Driemel, M. van Kreveld, and V. Sacristán.An algorithmic framework for segmenting trajectories based on spatio-temporal criteria. In Proceedings of the 18th SIGSPATIAL International Conference on Advances inGeographic Information Systems, pages 202–211. ACM, 2010. 41/41