1. CROWDINSIDE: AUTOMATIC CONSTRUCTION OF INDOOR FLOOR PLANS
By Team 11
Ayesha Begum
Mounika Kolluri
Sravani Dhanekula

2. OUTLINE Motivation Problem Definition Related Work Contribution Method
Key Concepts
Validation/Results
Conclusion
References
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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?
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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.
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5. PROBLEM DEFINITION
To automatically construct floor plans virtually in buildings
around the world based on crowdsourcing.
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6. SOLUTION Approach is to leverage cellphones.
Users motion inside buildings reflect the building structure.
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7. GOAL
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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 SmartSLAM describes only the corridor layout of the building. No information about the number of rooms, shapes e.t.c
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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.
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10. METHOD
Method proposed is CrowdInside which 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.
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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
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12. Data Collection Module
Collected data are the measurements from
Accelerometers
Magnetometers
Gyroscopes
Also collects data from WiFi received signal strengths from access points.
GPS is queried with low duty cycle.
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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
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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.
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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.
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16. Anchor Points Versus Dead Reckoning
Time (sec)
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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.
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18. GPS based anchor points
Building Entrance
- Loss of GPS signal
Low duty cycle
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19. Inertial based Anchor points
Differentiates 5 different categories
Elevator
Escalator
Stairs
Walking
Stationary
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20. Inertial based anchor points (Contd..)
ELEVATOR
It has a unique and repeatable pattern
Finite State Machine
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21. Inertial based anchor points (Contd…)
ESCALATOR
Constant Velocity
Distinguished from stationary by variance of magnetic field effecting the smart phone
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22. Variance of acceleration
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23. Inertial based anchor points (Contd..)
STAIRS
- Value of correlation between Z and Y axes acceleration is used to separate stairs and walking
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24. Inertial based anchor points (Contd..)
Stairs Up and Down
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25. Complete classification tree
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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.
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27. Floor Plan Estimation (Contd..)
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28. Floor Plan Estimation (Contd..)
Detailed floor plan
- Traces segmentation and filtering
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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
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30. Separation of adjacent rooms
Segments clustering
- Distance between
segments mid points
- Nearby rooms cannot
be separated by spatial
distances.
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31. Floor Plan Estimation (Contd..)
Measured WiFi signals similarity
- Used to separate adjacent rooms
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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.
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33. Floor Plan Estimation (Contd..)
Intersection Points
Estimated door locations
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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
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35. Anchor Points Estimation Accuracy
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36. Anchor Extraction Module
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37. Inertial based Anchor Points
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38. Performance of anchor detection
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39. Floor Plan Estimation Accuracy
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40. Demo Of Crowd Inside
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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 incentives etc.
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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,
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