1. Sensors Fusion for Mobile Robotics localization

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???
Need more/better pictures

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

4. Robot Sensors - localization
Good features
Bad features
Need more/better pictures
Incremental vs Absolute

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

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

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

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

9. Uncerainty Estimation – Sensor Fusion
Using uncertainty: Sensor Fusion
Uncerainty Estimation – Sensor Fusion

10. Questions?

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

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

13. Incremental an Global Localization
Incremental vs Global

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

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

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

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

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

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

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

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

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

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

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

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

26. Delay

28. List of symbols:

29. X is the pose (position and attitude)