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72 lines
1.9 KiB
ReStructuredText
72 lines
1.9 KiB
ReStructuredText
.. _localization:
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Localization
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============
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.. include:: extended_kalman_filter_localization.rst
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Unscented Kalman Filter localization
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------------------------------------
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This is a sensor fusion localization with Unscented Kalman Filter(UKF).
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The lines and points are same meaning of the EKF simulation.
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Ref:
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- `Discriminatively Trained Unscented Kalman Filter for Mobile Robot
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Localization`_
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Particle filter localization
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----------------------------
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This is a sensor fusion localization with Particle Filter(PF).
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The blue line is true trajectory, the black line is dead reckoning
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trajectory,
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and the red line is estimated trajectory with PF.
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It is assumed that the robot can measure a distance from landmarks
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(RFID).
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This measurements are used for PF localization.
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Ref:
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- `PROBABILISTIC ROBOTICS`_
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Histogram filter localization
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-----------------------------
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This is a 2D localization example with Histogram filter.
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The red cross is true position, black points are RFID positions.
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The blue grid shows a position probability of histogram filter.
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In this simulation, x,y are unknown, yaw is known.
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The filter integrates speed input and range observations from RFID for
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localization.
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Initial position is not needed.
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Ref:
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- `PROBABILISTIC ROBOTICS`_
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.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
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.. _Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization: https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization
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.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif
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.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif
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.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif
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