HiQuadLoc: An RSS-Based Indoor Localization System for High-Speed Quadrotors 1 Tuo Yu*, Yang Zhang*,...

HiQuadLoc: An RSS-Based Indoor Localization System for High-Speed Quadrotors

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Tuo Yu*, Yang Zhang*, Siyang Liu*, Xiaohua Tian*, Xinbing Wang*, Songwu Lu**

*Shanghai Jiao Tong University

**University of California at Los Angeles

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Measurement and Evaluation

Conclusion

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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.

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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. 7093-7098, Jun. 2012.

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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. 591-602, 2009.

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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.

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Motivation

The existing Wi-Fi RSS-based indoor localization systems cannot be directly applied to locate high-

speed quadrotors for the following reasons:

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.

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Localization resultMeasurement

Position

Motivation

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.

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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.

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Measurement and Evaluation

Conclusion

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System Architecture

The system consists of quadrotor (smartphone) and localization server.

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Outline

Motivation

System Architecture

Working Process

Off-line Stage

On-line Stage

Localization Algorithm

Measurement and Evaluation

Conclusion

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Working Process: Off-line Stage

① Divide the localization region into cubes with constant size.

② Measure RSS at 1 of each 8 cubes only.

③ Upload the data to the localization server.

④ Process the data according to the 4-D RSS Interpolation Algorithm.

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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.

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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

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Working Process: Off-line Stage

Thus we can use the cubic spline interpolation in the 4-D space to estimate

when .

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Before Interpolation After Interpolation Real Fingerprints

Working Process: On-line Stage

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.

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Working Process: On-line Stage

① The quadrotor sends a message to the server including the length of time slot T.

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.

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Working Process: On-line Stage

① The quadrotor sends a message to the server including the length of time slot T.

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 The localization server estimates the position of the quadrotor, and sends the result back.

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Working Process: On-line Stage

① The quadrotor sends a message to the server including the length of time slot T.

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 The localization server estimates the position of the quadrotor, and sends the result back.

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 .

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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.

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Working Process: On-line Stage

During a flight, the Turning Detector periodically measures XXXX processes them by low-pass

filter, and calculates

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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.

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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.

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Preliminary Localization Algorithm

Path Estimation

Path Fitting

Location Prediction

Measurement and Evaluation

Conclusion25

Localization Algorithm

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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.27

4-D RSS Interpolation AlgorithmThe result of each RSS measurement operated by the quadrotor

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Preliminary Localization Algorithm

Path Estimation

Path Fitting

Location Prediction

Measurement and Evaluation

Conclusion28

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:

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Path Estimation

Kalman Filter (process)

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Preliminary localization result

Filtered Location

Tk k kQ E w w

Tk k kR E u u

Path Estimation

Parameter Readjustment During Turning

One disadvantage of Kalman filter is that the localization accuracy at corners decreases obviously.

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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.

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Tk k kR E u u

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Preliminary Localization Algorithm

Path Estimation

Path Fitting

Location Prediction

Measurement and Evaluation

Conclusion33

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.

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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.

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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.

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Quadratic polynomial fitting

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Preliminary Localization Algorithm

Path Estimation

Path Fitting

Location Prediction

Measurement and Evaluation

Conclusion37

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 .

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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.

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Measurement and Evaluation

Conclusion

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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.

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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

Evaluation of Localization Algorithms

Space: 675*4; AP: 2*4 E5776.

Fingerprints: collect the RSS data from 484 (18%) of cubes.

Use cameras to record the real location of the quadrotor.

The test is repeated for 10 times, and 1268 localization results are recorded.

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Measurement and Evaluation

Evaluation of Localization Algorithms

Compared with normal RSS-based systems, HiQuadLoc has reduced the location error by 62.8%.

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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.

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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.

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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.

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Outline

Motivation

System Architecture

Working Process

Localization Algorithm

Measurement and Evaluation

Conclusion

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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.

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END

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