Skip to content

Latest commit

 

History

History
45 lines (37 loc) · 1.93 KB

README.md

File metadata and controls

45 lines (37 loc) · 1.93 KB

License: GPL v3

probabilistic-robotics-python-examples

Alternative text Alternative text

This repository contains Python script and notebooks to reproduce practical examples of popular algorithms presented in the Probabilistic Robotics book for didactic activities.

Content organization (work in progress)

  • Motion Models:
    • Odometry Motion Model
    • Velocity Motion Model
  • Sensors Models:
    • Beam Range Model
    • Likelihood Fields
    • Landmark Model
    • utils algorithms: ray casting, grid map utils, generate beam data
  • Gaussian Filters:
    • Extended Kalman Filter: ekf, ekf_robot_sim
    • Unscented Kalman Filter: ukf, ukf_robot_sim (TO DO: improve numerical stability)
    • probabilistic models:
    • utils: utility functions (residual, mean, metrics), plot_utils

Alternative text

References

@book{10.5555/1121596,
author = {Thrun, Sebastian and Burgard, Wolfram and Fox, Dieter},
title = {Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)},
year = {2005},
isbn = {0262201623},
publisher = {The MIT Press}
}

Acknowledgements

This work has been realized thanks to a joint effort by researchers at PIC4SeR Centre for Service Robotics at Politecnico di Torino (https://pic4ser.polito.it/). It supports the didactic activity of the course (Sensors, embedded systems and algorithms for Service Robotics) offered from 2023/24 in the M.Sc. in Mechatronic Engineering at Politecnico di Torino.