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“Mapping while walking” 1 Saehoon Yi 1, Piotr Mirowski 2,3, Tin Kam Ho 2,4, Vladimir Pavlovic 1 1 Computer Science Department, Rutgers University 2 Statistics.

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Presentation on theme: "“Mapping while walking” 1 Saehoon Yi 1, Piotr Mirowski 2,3, Tin Kam Ho 2,4, Vladimir Pavlovic 1 1 Computer Science Department, Rutgers University 2 Statistics."— Presentation transcript:

1 “Mapping while walking” 1 Saehoon Yi 1, Piotr Mirowski 2,3, Tin Kam Ho 2,4, Vladimir Pavlovic 1 1 Computer Science Department, Rutgers University 2 Statistics and Learning Research Department, Bell Labs, Alcatel-Lucent 3 Now at Microsoft Bing 4 Now at IBM Watson Pose Invariant Activity Classification for Multi-floor Indoor Localization

2 Outline 2  Related work  Indoor localization Pedestrian Dead Reckoning GraphSLAM  Motivation  “Mapping while walking”  Methods  Pose-invariant sensor features  SVM classification of actions  HMM temporal smoothing  Results

3 Indoor Localization  Indoor localization  Various practical use Radio Frequency (WiFi, 4G cell) maps “where am I in this building?” Network deployment optimization: “where should we place that 4G cell in the building?”  No GPS  Smartphone sensors Accelerometer Gyroscope Magnetometer Barometer WiFi, cell 3

4 Pedestrian Dead Reckoning  Step detection on vertical acceleration  [Steinhoff et al., Pervasive Computing and Communications 2010]  3-axis orientation [Madgwick et al., Rehabilitation Robotics 2011]  Trajectory updates  step length d, offset angle β  Subject to drift due to orientation error  Sensor measurement noise  Magnetic field perturbations 4 x t-5 x t-4 x t-3 x t-2 x t-1 xtxt x t+1 error at position reset direction of walk angle offset beta β Yaw of the phone w.r.t. horizontal X axis longitude or human/horizontal X (“towards East”)

5 GraphSLAM 5  Modify trajectory to minimize estimation error  [Grisetti et al., Transportation Systems Magazine 2010]  Challenges  Requires landmark detection How do we know that two different observations are actually taken at the same location? Hand-placed landmarks, e.g.,: QR code or NFC tag Manually installed and maintained xjxj z ij xixi xj*xj*

6 Motivation 6  Detect and provide natural landmark for GraphSLAM  Stairs and elevators are accurately detected  They are non moving, distinct structures, which is ideal for landmarks  Classify human activities using a smartphone in the pocket  Pose invariant features extracted from smartphone sensors  Jointly infer activity and floor information  Focus on activities that incur floor change  “Mapping while walking”  Facilitate radio-frequency map building for network engineers

7 Methods 7  SVM: Classify activity at each time point  HMM: Smoothing SVM activity classification and jointly infering floor  Activities  walking  stair up  stair down  stand still  elevator up  elevator down

8 Pose invariant features for IMU sensors 8  Pose invariant features from A  [Kobayashi et al., ICASSP, 2011]  Invariant to rotation R

9 Statistical features for barometer 9  Rotation does not affect air pressure  Fluctuate over time  Depend on weather and temperature  Detects ascending/descending air pressure

10 SVM classification 10  Features are extracted from sliding window of sensor observations  Linear SVM for 6 activity classes  Fast and storage efficient Linear classification  Able to implement real time classification in Android OS  Provide activity probability  Platt’s scaling algorithm  Required to obtain HMM observation probability

11 HMM smoothing 11  6 activities for each floor  State transit for strong evidence  Smooth sporadic brief misclassification  Augment inference of activity with floor from Viterbi algorithm  Observation probability  from SVM confidence level(Platt’s scaling)  from mixture of Gaussian

12 HMM smoothing 12  Transition probability  Manually design transition probabilities Higher probability of transition to the same state Floor changes only for stair and elevator

13 Experiment set up 13  Input data  Sensor data recorded at 50Hz  Feature extraction  Sliding window IMU sensor features Length: 64 frames Step size: 35 frames Barometer features Length: 192 frames  Train data: seconds of each class repeatedly performed  Test data: 6160 seconds of 12 natural sequence

14 Activity classification result 14  For SVM,  Walking is confused to taking stairs  Standing still is confused to taking elevators Leg dynamics are similar Air pressure does not change over short period of time Each sliding window is considered independently

15 Rectified HMM inference 15  Post rectification  HMM allows: walking → taking stair → walking on the same floor  Taking stairs w/o floor change → walking  Taking elevators w/o floor change → stand still

16 Activity classification result 16  HMM removes sporadic misclassification between walking and taking stairs  Rectification: rectifies stairs to walking when it does not incur floor change

17 Stability of HMM transition model 17  p: Probability of state transition  Fairly stable with p  HMM slightly degrades with high p  Lower probability of staying at the same state

18 Landmarks match 18  Types of landmark  Stair  Elevator  GraphSLAM requires matching of the same landmark along the trajectory  Training phase  Obtain information from reference landmarks WiFi access point visibility  Testing phase  Landmarks matching Get current WiFi AP visibility Calculate distance to reference landmarks Take the closest corresponding landmark

19 Initial PDR trajectory 19  Initial trajectory obtained from PDR  Rotation angle underestimated for every turn  Need to be modified using GraphSLAM

20 Multi-floor GraphSLAM result 20

21 Conclusion 21  Rotation invariant features were able to capture different dynamics of motion activities  Our approach improves classification accuracy and jointly infers activity and corresponding floor information  GraphSLAM successfully modifies multi-floor trajectory using natural landmarks detected by our framework.

22 Q & A 22 Thank you


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