probabilistic-robotics-python-examples

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

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.