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State estimation with fleeting data

State estimation with fleeting data. Introduction Approach used: fleeting data and tubes Demo Conclusion. > Plan. Introduction. Introduction. Context: offline SLAM for submarine robots using interval arithmetic and constraint propagation (without outliers). Introduction.

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State estimation with fleeting data

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  1. State estimation with fleeting data

  2. Introduction • Approach used: fleeting data and tubes • Demo • Conclusion > Plan

  3. Introduction

  4. Introduction • Context: offline SLAM for submarine robots using interval arithmetic and constraint propagation (without outliers)

  5. Introduction • Redermor and Daurade, submarine robots of the GESMA

  6. Introduction • Sensors of the submarines : • GPS (position at the surface) • DVL (speed and altitude) • IMU (Euler angles and rotating speeds) • Pressure sensor (depth) • Lateral sonar

  7. Introduction

  8. Introduction • Experiments with marks in the sea

  9. Introduction • Goals: • Get an envelope for the trajectory of the robot • Compute sets which contain marks • Make a cartography of the area

  10. 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. Introduction • Output: • Trajectory (envelope and center) • Position of marks in the sea (envelope and center) • Map of the area

  12. Introduction • 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)

  13. Introduction • 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

  14. Introduction

  15. Introduction • 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

  16. Introduction

  17. Introduction • 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

  18. Approach used: fleeting data and tubes

  19. Demo

  20. Conclusion

  21. 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. Conclusion • Prospects : • Do SLAM instead of localization • Apply the method on real sonar data of submarines

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