HiQuadLoc: An RSS-Based Indoor Localization System for High-Speed Quadrotors 1 Tuo Yu*, Yang Zhang*, Siyang Liu*, Xiaohua Tian*, Xinbing Wang*, Songwu Lu** *Shanghai Jiao Tong University **University of California at Los Angeles

2 Outline Motivation System Architecture Working Process Localization Algorithm Measurement and Evaluation Conclusion 2

Motivation   In the field of indoor localization and navigation, since GPS is not available, how to locate fast-moving UAVs (Unmanned Aerial Vehicles) such as quadrotors is still a challenging topic.   Most of the previous works are based on vision or ultrasound detection. 3

Motivation   J. Eckert, R. German and F. Dressler, “On autonomous indoor flights: High-quality real-time localization using low- cost sensors,” in IEEE ICC, pp , Jun

Motivation   F. Kendoul, I. Fantoni and K. Nonami, “Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles,” in Robotics and Autonomous Systems, vol. 57(6), pp ,

Motivation   In the field of indoor localization and navigation, since GPS is not available, how to locate fast-moving UAVs such as quadrotors is still a challenging topic.   Most of the previous works are based on vision or ultrasound detection.   Additional infrastructures such as off-board sensors and cameras are still needed, which leads to extra cost and energy consumption.   We aim to apply the Wi-Fi fingerprint-based method, one of the most widely used technologies in indoor localization. 6

Motivation   The existing Wi-Fi RSS-based indoor localization systems cannot be directly applied to locate high-speed quadrotors for the following reasons: ① ① Flight speed impacts localization accuracy severely.   An RSS measurement will take at least 0.1s to 1s, during which the quadrotors (35km/h) would have moved for 1m to 10m. 7 Localization result MeasurementPosition

Motivation ② ② The workload of measuring all the RSS data in 3-D space is much higher than that in 2-D case.   Some technologies such as surface-based interpolation only record the average value of RSS at each calibration point, which loses the information included in the statistical features of RSS caused by the complex channel environment. 8

Motivation   We need to:   Estimate the real flight path of a quadrotor with the limited number of times for RSS measurement;   Consider the reduction in accuracy caused by the communication delay between the quadrotor and the server;   Estimate the probability distributions of RSS values at most cubes, instead of estimating the average values of RSS at these cubes only. 9

10 Outline Motivation System Architecture Working Process Localization Algorithm Measurement and Evaluation Conclusion 10

System Architecture   The system consists of quadrotor (smartphone) and localization server. 11

12 Outline Motivation System Architecture Working Process Off-line Stage On-line Stage Localization Algorithm Measurement and Evaluation Conclusion 12

Working Process: Off-line Stage ① ① Divide the localization region into cubes with constant size. ② ② Measure RSS at 1 of each 8 cubes only. ③ ③ Upload the data to the localization server. ④ ④ Process the data according to the 4-D RSS Interpolation Algorithm. 13

Working Process: Off-line Stage   4-D RSS Interpolation Algorithm   : a cube with coordinate.     We collect training data for 1 of each 8 cubes:   s = 1,…,M and M is the number of APs; N is the number of training data at each cube. 14

Working Process: Off-line Stage   The probability for to appear at is   : a cube in the fingerprint map generated by AP s.   Since p( ) is constant, let. It is continuous in the 4-D space   We have gotten the values when 15

Working Process: Off-line Stage   Thus we can use the cubic spline interpolation in the 4-D space to estimate when. 16 Before InterpolationAfter InterpolationReal Fingerprints

Working Process: On-line Stage ① ① The quadrotor sends a message to the server including the length of time slot T.   Note that T must be longer than the minimal RSS measurement time according to the quadrotor’s hardware performance. 17

Working Process: On-line Stage ① ① The quadrotor sends a message to the server including the length of time slot T. ② ② In each time slot, the quadrotor measures RSS, and sends a message including the data to the server. The message also contains:   The value of communication delay measured in the last localization process.   Whether the quadrotor is turning. 18

Working Process: On-line Stage ① ① The quadrotor sends a message to the server including the length of time slot T. ② ② In each time slot, the quadrotor measures RSS, and sends a message including the data to the server. The message also contains:   The value of communication delay measured in the last localization process.   Whether the quadrotor is turning. ③ ③ The localization server estimates the position of the quadrotor, and sends the result back. 19

Working Process: On-line Stage ① ① The quadrotor sends a message to the server including the length of time slot T. ② ② In each time slot, the quadrotor measures RSS, and sends a message including the data to the server. The message also contains:   The value of communication delay measured in the last localization process.   Whether the quadrotor is turning. ③ ③ The localization server estimates the position of the quadrotor, and sends the result back. ④ ④ The quadrotor calculates the time interval between the sending out of the message and the return of the result, and sets it as the new. 20

Working Process: On-line Stage   Turning Detection Using Direction Sensor   It is hard for the client to detect the flight direction of quadrotors because a quadrotor can make lateral movements without changing its head direction.   We notice that when a quadrotor is moving in a specific direction, its normal vector will have a drift angle for the same direction. 21

Working Process: On-line Stage   During a flight, the Turning Detector periodically measures XXXX processes them by low-pass filter, and calculates 22

Working Process: On-line Stage   If for continuous duration, the quadrotor is hovering, during which it may change its direction (turning starts).   Once for another, the turning ends. 23

Working Process: On-line Stage   Once and for more than, the quadrotor is turning. is the mean value for the recent values of α.   When and for more than, the turning is ended. 24

25 Outline Motivation System Architecture Working Process Localization Algorithm Preliminary Localization Algorithm Path Estimation Path Fitting Location Prediction Measurement and Evaluation Conclusion 25

Localization Algorithm 2626 preliminary localization algorithm path estimation path fitting location prediction

Preliminary Localization Algorithm   The algorithm is based on a frequently-used probabilistic model.   Find the largest in. Its corresponding denotes the estimated location for the quadrotor D RSS Interpolation Algorithm The result of each RSS measurement operated by the quadrotor

28 Outline Motivation System Architecture Working Process Localization Algorithm Preliminary Localization Algorithm Path Estimation Path Fitting Location Prediction Measurement and Evaluation Conclusion 28

Path Estimation   The path estimation method is based on Kalman Filter.   The state of the quadrotor in the time slot:   The motion model:   The preliminary localization model: 29

Path Estimation   Kalman Filter (process) 3030 Preliminary localization result Filtered Location

Path Estimation   Parameter Readjustment During Turning   One disadvantage of Kalman filter is that the localization accuracy at corners decreases obviously. 3131

Path Estimation   Parameter Readjustment During Turning   Once the server receives a turning-start signal, the following steps are executed:   Reducing leads to a higher weighting factor for the newest result. 3232

33 Outline Motivation System Architecture Working Process Localization Algorithm Preliminary Localization Algorithm Path Estimation Path Fitting Location Prediction Measurement and Evaluation Conclusion 33

Path Fitting   Kalman filter works well only when its parameters match with the real case, which can hardly be satisfied since the indoor environment varies.   The method of curve fitting is based on the assumption that a high-speed quadrotor tends to move in nearly straight lines. (The case of turning has been considered.)   The computation complexity of 3-D curve fitting is large and is severely impacted by the initial parameters of the curvilinear function.   We focus on the projected curve of the flight path on the 2- D ground. 34

Path Fitting   To avoid the case that the flight path is perpendicular to the X-axis or the Y -axis, we exchange the two axes and choose the fitting function with the maximum correlation coefficient. 35

Path Fitting   To avoid the case that the flight path is perpendicular to the X-axis or the Y-axis, we exchange the two axes and choose the fitting function with the maximum correlation coefficient. 36 Quadratic polynomial fitting

37 Outline Motivation System Architecture Working Process Localization Algorithm Preliminary Localization Algorithm Path Estimation Path Fitting Location Prediction Measurement and Evaluation Conclusion 37

Location Prediction   Due to the communication delay between the client and the server, there still exists significant localization error.   The delay has been uploaded.   contains the estimated velocity vector of the quadrotor.   Extend the motion curve of the quadrotor by   The server replies the client with the final localization result. 38

Location Prediction   Due to the communication delay between the client and the server, there still exists significant localization error.   The delay has been uploaded.   contains the estimated velocity vector of the quadrotor.   Extend the motion curve of the quadrotor by   The server replies the client with the final localization result.   Note that the computation complexity of the whole algorithm is only proportional to the times of RSS measurements in a path section, which are usually O(1). Thus the response time of the server is bounded. 39

40 Outline Motivation System Architecture Working Process Localization Algorithm Measurement and Evaluation Conclusion 40

Measurement and Evaluation   Evaluation of 4-D RSS Interpolation Algorithm   Space: ; AP: 4 E586Bs-2 at the four corners.   Full fingerprints: uses all the data collected from all the 405 cubes for localization.   Interpolated fingerprints: uses the data collected from 75 cubes for localization. 41   The interpolation reduces the accuracy of localization by 0.10m to 0.17m   The workload of fingerprint collection can be reduced by 81%.

Measurement and Evaluation 42

  Evaluation of Localization Algorithms   Compared with normal RSS-based systems, HiQuadLoc has reduced the location error by 62.8%. 43

Measurement and Evaluation   Evaluation of Parameter Readjustment During Turning   We change the values of respectively.   We analyze alone to rule out the additional gain of other methods.   We focus on the ±5 localization results around each corner. 44   When the error is minimum when XXXXXX respectively.   It is shown that changing the parameters of Kalman filter is necessary.

Measurement and Evaluation   Evaluation of HiQuadLoc for Different Flight Speeds   We control the quadrotor to fly in a straight line for different speeds: 3m/s, 2m/s and 1m/s. 45   3m/s: average error: 2.19m, reduced by 53.0%   2m/s: average error: 1.76m, reduced by 51.0%.   1m/s, average error: 0.89m, reduced by 66.4%.   Since the location error caused by delay is severer in the higher- speed case, the contribution of the location prediction method is more obvious than that in the lower-speed case.

46 Outline Motivation System Architecture Working Process Localization Algorithm Measurement and Evaluation Conclusion 46

Conclusion   Our contributions:   An RSS-based indoor localization system which can be applied on quadrotors moving at high speed.   The methods of path estimation, path fitting and location prediction to improve accuracy.   A 4-D RSS interpolation algorithm to reduce the workload by more than 80% during the offline data training phase.   The results of experiments show that HiQuadLoc reduces the average location error by more than 50% compared with normal RSS-based systems. 4747

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