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

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

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

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

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

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

GOAL 6/41

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 7/41

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

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. 9/41

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

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. 11/41

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

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

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

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

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

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

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

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

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

Variance of acceleration 21/41

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

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

Complete classification tree 24/41

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

Floor Plan Estimation (Contd..) 26/41

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

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

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

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

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

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

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

Anchor Points Estimation Accuracy 34/41

Anchor Extraction Module 35/41

Inertial based Anchor Points 36/41

Performance of anchor detection 37/41

Floor Plan Estimation Accuracy 38/41

Demo Of Crowd Inside 39/41

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. 40/41

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