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Antonin RAFFIN
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docs/modules/localization.rst
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docs/modules/localization.rst
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.. _localization:
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Localization
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============
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Extended Kalman Filter localization
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-----------------------------------
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.. raw:: html
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<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/extended_kalman_filter/animation.gif" width="640">
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This is a sensor fusion localization with Extended Kalman Filter(EKF).
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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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the green point is positioning observation (ex. GPS), and the red line
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is estimated trajectory with EKF.
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The red ellipse is estimated covariance ellipse with EKF.
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Ref:
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- `PROBABILISTIC ROBOTICS`_
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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/PythonRobotics/raw/master/Localization/unscented_kalman_filter/animation.gif
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.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/particle_filter/animation.gif
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.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/histogram_filter/animation.gif
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43
docs/modules/mapping.rst
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docs/modules/mapping.rst
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.. _mapping:
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Mapping
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=======
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Gaussian grid map
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-----------------
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This is a 2D Gaussian grid mapping example.
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Ray casting grid map
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--------------------
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This is a 2D ray casting grid mapping example.
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|3|
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k-means object clustering
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-------------------------
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This is a 2D object clustering with k-means algorithm.
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Object shape recognition using circle fitting
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---------------------------------------------
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This is an object shape recognition using circle fitting.
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The blue circle is the true object shape.
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The red crosses are observations from a ranging sensor.
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The red circle is the estimated object shape using circle fitting.
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.. |2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/gaussian_grid_map/animation.gif
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.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/raycasting_grid_map/animation.gif
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.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/kmeans_clustering/animation.gif
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.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/circle_fitting/animation.gif
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104
docs/modules/slam.rst
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docs/modules/slam.rst
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.. _slam:
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SLAM
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====
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Simultaneous Localization and Mapping(SLAM) examples
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.. _iterative-closest-point-(icp)-matching:
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Iterative Closest Point (ICP) Matching
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--------------------------------------
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This is a 2D ICP matching example with singular value decomposition.
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It can calculate a rotation matrix and a translation vector between
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points to points.
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|3|
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Ref:
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- `Introduction to Mobile Robotics: Iterative Closest Point Algorithm`_
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EKF SLAM
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--------
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This is an Extended Kalman Filter based SLAM example.
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The blue line is ground truth, the black line is dead reckoning, the red
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line is the estimated trajectory with EKF SLAM.
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The green crosses are estimated landmarks.
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|4|
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Ref:
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- `PROBABILISTIC ROBOTICS`_
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FastSLAM 1.0
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------------
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This is a feature based SLAM example using FastSLAM 1.0.
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The blue line is ground truth, the black line is dead reckoning, the red
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line is the estimated trajectory with FastSLAM.
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The red points are particles of FastSLAM.
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Black points are landmarks, blue crosses are estimated landmark
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positions by FastSLAM.
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|5|
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Ref:
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- `PROBABILISTIC ROBOTICS`_
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- `SLAM simulations by Tim Bailey`_
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FastSLAM 2.0
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------------
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This is a feature based SLAM example using FastSLAM 2.0.
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The animation has the same meanings as one of FastSLAM 1.0.
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|6|
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Ref:
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- `PROBABILISTIC ROBOTICS`_
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- `SLAM simulations by Tim Bailey`_
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Graph based SLAM
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----------------
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This is a graph based SLAM example.
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The blue line is ground truth.
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The black line is dead reckoning.
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The red line is the estimated trajectory with Graph based SLAM.
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The black stars are landmarks for graph edge generation.
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|7|
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Ref:
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- `A Tutorial on Graph-Based SLAM`_
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.. _`Introduction to Mobile Robotics: Iterative Closest Point Algorithm`: https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf
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.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
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.. _SLAM simulations by Tim Bailey: http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm
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.. _A Tutorial on Graph-Based SLAM: http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
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.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/iterative_closest_point/animation.gif
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.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/EKFSLAM/animation.gif
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.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/FastSLAM1/animation.gif
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.. |6| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/FastSLAM2/animation.gif
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.. |7| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/GraphBasedSLAM/animation.gif
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