Sensors Fusion for Mobile Robotics localization. M. De Cecco - Robotics Perception and Action

Transcription

1 Sensors Fusion for Mobile Robotics localization 1

2 Until now we ve presented the main principles and features of incremental and absolute (environment referred localization systems) could you summarize the main features and differences??? Main problems of both categories??? 2

3 Robot Sensors - localization Do you recognize the difference between the two categories? Infrared Ranging Magnetometer GPS IR Modulator Receiver Accelerometer Linear Encoder Camera Sonar Ranging Gyroscope Rotary Encoder Compass Laser triangulation Laser Rangefinder Incremental vs Absolute 3

4 Robot Sensors - localization Good features Bad features Incremental vs Absolute 4

5 Example: localization with encoders Example: the vehicle shall be driven on a corridor localized only by encoders mounted on the wheels. Problem: Left wheel smaller radius (wrt to the nominal value). Ideal = the path that the vehicle assumes to lie on Drift 5

6 By estimating the uncertainty it is possible to detect and avoid accident but also to combine information Importance of Uncertainty Estimation 6

7 Event: vehicle localization with another sensor referred to the environment (for example a laser triangulation, a camera, etc) Uncertainty Estimation Sensor Fusion 7

8 Without using uncertainty: simple average Uncertainty Estimation Sensor Fusion 8

9 Using uncertainty: Sensor Fusion Uncerainty Estimation Sensor Fusion 9

10 Questions? 10

11 Incremental vs Global Localization Vehicle localization main classification : INCREMENTAL LOCALIZATION The current vehicle pose at time t is evaluated wrt the information achieved in the previous localization at time t- 1. GLOBAL LOCALIZATION The current vehicle pose at time t is evaluated wrt the information referred to a global reference system. Incremental vs Global 11

12 Incremental an Global Localization Σ0: global reference system Vehicle is globally localized with a direct estimation of H0,k. Vehicle is incrementally localized using the concatenation of the estimations Hi,j. Incremental vs Global 12

13 Incremental an Global Localization Incremental vs Global 13

14 Sensor (most used one) classification: INCREMENTAL LOCALIZATION GLOBAL LOCALIZATION Encoders on wheels Triangulation Systems Gyroscope + magnetometers Ultrasound beacon Laser Scanner comparison with previous acquisition Laser Scanner comparison with a map Camera looking on the floor Camera looking on the ceiling Incremental vs Global 14

15 Incremental an Global Localization Feature INCREMENTAL GLOBAL Drift in pose estimation HIGH NO Measurement update rate HIGH LOW Repeatability HIGH LOW NO YES Needs of environment information Incremental vs Global 15

16 Odometric - Global Navigation Fusion First issue: time alignment due to the different update rate Incremental- Global Localization Sensor Fusion 16

17 Odometric - Global Navigation Fusion First issue: time alignment due to the different update rate Incremental- Global Localization Sensor Fusion 17

18 Odometric - Global Navigation Fusion Second issue: Sensor Fusion and how to continue! Example Matlab Incremental- Global Localization Sensor Fusion 18

19 Incremental and Global Localization Feature INCREMENTAL GLOBAL SENSOR FUSION Drift in pose estimation HIGH NO NO Measurement update rate HIGH LOW HIGH Repeatability HIGH LOW HIGH, SMOOTH TRAJECTORY NO YES YES Needs of environment information Incremental- Global Localization Sensor Fusion 19

20 Example: Use of encoders + gyro + laser triangulation my first industrial AGV 20

21 1 STEP (a) 1 STEP (b) Ø Encoders Ø Gyro 2 STEP No drift Low repeatability (especially in motion or with low number of reflectors) High frequency of update Drift 1 & 2 STEP: Ø Laser triangulation High frequency of update & No drift Sensor Fusion 21

22 1 STEP (a) Fusion between incremental systems * x R calibrated as a Function of the manoeuvre Kinematics equations Fusion of the increments already seen this example 22

23 1 STEP (b) Real time covariance estimation X is the POSE (position and attitude) * White noise This part takes into account correlation as a function of time wk vector of the uncertainty parameters 23

24 2 STEP (a) Estimation of covariance of laser triangulation as a function of the manoeuvre 1. State of the encoders 2. Laser quality factor * 2 STEP (b) Fusion between environment referred and incremental estimations 24

25 C.I. 30 sigma C.I. 2 sigma 25

26 Delay Delay 26

27 27

28 List of symbols List of symbols: (x,y) the sensor fusion estimated position with respect to the fixed reference of the reference point P r on the vehicle (xe,ye) the driver wheel encoder estimated position with respect to the fixed reference of the reference point Pr on the vehicle Xk (x,y,d ) position and attitude vector CV covariance matrix of the vector V R the driver wheel radius ICR Instantaneous Centre of Rotation a 0 the steering angle when the ICR is at the infinity a k the steering angle with respect to a 0 n k the number of counts from the driving encoder n0 the number of counts from the driving encoder in one turn d (t) the vehicle with respect to the fixed reference E d (t) the encoder estimated attitude of the vehicle with respect to the fixed reference G d (t) the gyro estimated attitude of the vehicle with respect to the fixed reference b the distance between the rotation axis of the driver wheel and the axis of the back wheel which leads the manoeuvre VG(t) the gyro voltage output Tc the sampling period G() the gyro characteristic DF() the algorithm of Data Fusion df Jacobian of the vector function F s l the standard uncertainty in parameter l e l the uncertainty in parameter l defined with a coverage factor of two 28

29 X is the pose (position and attitude) 29