Indoor Follow Me Drone
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1 Indoor Follow Me Drone Wenguang Mao, Zaiwei Zhang, Lili Qiu, Jian He, Yuchen Cui, Sangki Yun Presented By Stephen Xia for Columbia University ELEN 9705 Spring 2018 Instructor: Prof. Xiaofan (Fred) Jiang January 24, 2018 Proceedings of the 15 th Annual Internation Conference on Mobile Systems, Applications, and Services MobiSys 2017, Niagara Falls, NY, USA June 19 th 23 rd, 2017
2 Motivation Follow Me Drones Problem: There is no robust and efficient mobile solution for indoor follow me
3 Components of Follow Me User Tracking Control Drone Control User movement Prediction
4 Components of Follow Me User Tracking Control Drone Control User movement Prediction
5 Current Methods for User Tracking
6 GPS Current Methods for User Tracking
7 Current Methods for User Tracking GPS Computer Vision
8 Current Methods for User Tracking GPS Computer Vision RF
9 Prior Works for User Tracking GPS Computer Vision RF Acoustics
10 Limitations in Indoor Scenarios
11 Limitations in Indoor Scenarios GPS Availability
12 Limitations in Indoor Scenarios GPS Computer Vision Availability Computation and Accuracy
13 Limitations in Indoor Scenarios GPS Computer Vision RF Availability Computation and Accuracy Hardware
14 Limitations in Indoor Scenarios GPS Computer Vision RF Acoustics Availability Computation and Accuracy Hardware
15 Acoustic Tracking Distance Orientation and Angle
16 Acoustic Tracking Prior Art Phase Shift Methods (e.g. Doppler Shift) Angle of Arrival and Time Difference of Arrival (e.g. Cross Correlation) *
17 Acoustic Tracking Prior Art Frequency Modulated Carrier Wave (FMCW) (e.g. Doppler Shift) MUltiple Signal Classification (MUSIC) *
18 Acoustic Tracking Prior Art Frequency Modulated Carrier Wave MUltiple Signal Classification (MUSIC)
19 Acoustic Tracking Distance Orientation and Angle
20 Acoustic Tracking Distance Orientation and Angle
21 Frequency Modulated Carrier Wave
22 Improving Robustness of FMCW - MUSIC
23 Improving Robustness of FMCW - Other Filtering the MUSIC spectrum
24 Improving Robustness of FMCW - Other Frequency Compensation and Signal Chopping Kalman Filter
25 Rabbit
26 Acoustic Tracking Distance Orientation and Angle
27 Acoustic Tracking Angle
28 Components of Follow Me User Tracking Control Drone Control User movement Prediction
29 Components of Follow Me User Tracking Control Drone Control User movement Prediction
30 Prior Work on Control
31 Prior Work on Control Manipulated Variables: Pitch Yaw Roll Controlled Variables: Distance to user Orientation to user Lateral Velocity
32 Prior Work on Control Controller Types: Model Predictive Control PID Vector Field Control Many more.
33 System Control Sensing: Rabbit Robust Acoustic-Based Indoor Tracking
34 System Control Manipulated Variables: Pitch Yaw Roll Controlled Variables: Distance to user Orientation to user Lateral Velocity
35 Components of Follow Me User Tracking Control Drone Control User movement Prediction
36 User Movement Prediction Controller: Model Predictive Control Sensing Lateral Velocity Doppler Shift
37 Full System
38 Full System
39 Evaluation
40 Evaluation - Rabbit
41 Evaluation Full System
42 Evaluation Full System
43 Evaluation Real World Trajectory
44 Evaluation Resource Usage and Delay Power: 3% per 10 minutes on a Galaxy S7
45 Discussion
Indoor Follow Me Drone
Indoor Follow Me Drone Wenguang Mao, Zaiwei Zhang, Lili Qiu, Jian He, Yuchen Cui, and Sangki Yun The University of Texas at Austin, Hewlett Packard Labs {wmao,zaiwei9,lili,jianhe,yuchen93}@cs.utexas.edu,
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