1. State estimation with fleeting data

2. 05/12/20152 > Plan 1.Introduction 2.Approach used: fleeting data and tubes 3.Demo 4.Conclusion

3. State estimation with fleeting data 05/12/20153 Introduction

4. State estimation with fleeting data 05/12/20154  Context: offline SLAM for submarine robots using interval arithmetic and constraint propagation (without outliers) Introduction

5. State estimation with fleeting data 05/12/20155  Redermor and Daurade, submarine robots of the GESMA Introduction

6. State estimation with fleeting data 05/12/20156  Sensors of the submarines : –GPS (position at the surface) –DVL (speed and altitude) –IMU (Euler angles and rotating speeds) –Pressure sensor (depth) –Lateral sonar Introduction

7. State estimation with fleeting data 05/12/20157 Introduction

8. State estimation with fleeting data 05/12/20158  Experiments with marks in the sea Introduction

9. State estimation with fleeting data 05/12/20159  Goals: –Get an envelope for the trajectory of the robot –Compute sets which contain marks –Make a cartography of the area Introduction

10. State estimation with fleeting data 05/12/201510 Introduction  Input: –Navigation data of the submarine (Euler angles, depth, altitude, speed, some GPS positions) –Marks detections on the sonar image (distance) –Raw sonar image –Time and max error for each data

11. State estimation with fleeting data 05/12/201511 Introduction  Output: –Trajectory (envelope and center) –Position of marks in the sea (envelope and center) –Map of the area

12. State estimation with fleeting data 05/12/201512  What has already been done: –Compute an envelope for the trajectory of the robot –Compute sets which contain marks (detected by a human operator by scrolling the sonar image) Introduction

13. State estimation with fleeting data 05/12/201513  What has already been done: –Generate a reconstitution of the sonar image showing the estimated position of the marks on it (predicted stain) –Help the human operator for the detection/identification of the marks –Check the consistency of input data Introduction

14. State estimation with fleeting data 05/12/201514 Introduction

15. State estimation with fleeting data 05/12/201515  What could be done: –Use a little bit more the sonar image to eliminate zones where we are sure there is no object (no bright points) Should improve the precision of the positions estimation Could help the user to see which parts of the sonar data might contain objects Introduction

16. State estimation with fleeting data 05/12/201516 Introduction

17. State estimation with fleeting data 05/12/201517  What could be done: –Use a little bit more the sonar image to eliminate zones where we are sure there is no object (no bright points) Should improve the precision of the positions estimation Could help the user to see which parts of the sonar data might contain objects –Generate a cartography of the area using sonar data Introduction

18. State estimation with fleeting data 05/12/201518 Approach used: fleeting data and tubes

19. State estimation with fleeting data 05/12/201519 Demo

20. State estimation with fleeting data 05/12/201520 Conclusion

21. State estimation with fleeting data 05/12/201521 Conclusion  We can use the sonar/telemetric data to improve the estimation of the trajectory  Tubes are well suited to deal with estimation of trajectories

22. State estimation with fleeting data 05/12/201522 Conclusion  Prospects : –Do SLAM instead of localization –Apply the method on real sonar data of submarines