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Localization

Localization. Life in the Atacama 2004 Science & Technology Workshop January 6-7, 2005 Daniel Villa Carnegie Mellon Matthew Deans QSS/NASA Ames. Basic Description. Sensing INS: 3 axis accel, 3 axis gyro Sun sensor Encoders Inclinometer FOG Motion commands Estimation

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Localization

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  1. Localization Life in the Atacama 2004Science & Technology WorkshopJanuary 6-7, 2005 Daniel Villa Carnegie Mellon Matthew Deans QSS/NASA Ames

  2. Basic Description • Sensing • INS: 3 axis accel, 3 axis gyro • Sun sensor • Encoders • Inclinometer • FOG • Motion commands • Estimation • Kalman Filter • Nonlinear smoothing • Dedicated PC-104 stack. • Goals: • Accuracy 5% distance traveled • Orientation within 3° • Odometry within 2% of distance traveled NASA Ames Research CenterCarnegie Mellon

  3. Block Diagram NASA Ames Research CenterCarnegie Mellon

  4. Sun Sensor Now includes integrated inclinometer NASA Ames Research CenterCarnegie Mellon

  5. Sun Sensor NASA Ames Research CenterCarnegie Mellon

  6. Sun Sensor • Camera model error: • 0.5 pixel RMS • 0.250 RMS for 3 dimensions of rotation • Integration: • Problems with h/w and s/w integration • In the field: • A few degrees • Obvious systematic errors: calibration? NASA Ames Research CenterCarnegie Mellon

  7. Dead Reckon Estimator • Straightforward path integration • Relied only on data from sensors • Encoders • FOG • Roll-Pitch • Does not use: • IMU • Sun tracker NASA Ames Research CenterCarnegie Mellon

  8. Dead Reckon Results NASA Ames Research CenterCarnegie Mellon

  9. Kalman Filter system outputs(sensors) rover system inputs (speed, radius) _ predicted outputs(sensors) model predicted state (x, y, z, roll, pitch, yaw) K updated state + NASA Ames Research CenterCarnegie Mellon

  10. Kalman Results • NaN NASA Ames Research CenterCarnegie Mellon

  11. Nonlinear Smoothing • Performed when robot is stationary • Operates on a sub-sampled sensor dataset • Revises movement history • New pose and covariance fed back into filter NASA Ames Research CenterCarnegie Mellon

  12. Nonlinear Smoothing Results Simulation Filtering Smoothing • Heading correction propagates to corrected position NASA Ames Research CenterCarnegie Mellon

  13. Next Steps: • Sun Sensor: • Early specification of interfaces • Better coordination of efforts • Estimator work: • Kalman Filter debugging, improvements • Comparison of Kalman vs dead reckon • Real-time Linux kernel NASA Ames Research CenterCarnegie Mellon

  14. Next Steps: Visual Odometry Accuracy of ~0.1% 1mm over 1m 1cm at 5-10m Critical element of single cycle instrument placement Could enable some return to site/point capability NASA Ames Research CenterCarnegie Mellon

  15. Next Steps: Visual Odometry NASA Ames Research CenterCarnegie Mellon

  16. end NASA Ames Research CenterCarnegie Mellon

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