pierrepault/probabilistic_robotics resource

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1318 stars · 389 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of detailed solutions to exercises from the textbook 'Probabilistic Robotics' by Thrun, Burgard, and Fox, implemented in C++, Julia, MATLAB, and Python/NumPy. It includes working implementations of algorithms like FastSLAM and the Sparse Extended Information Filter (SEIF), demonstrated on the Victoria Park dataset, with a FastSLAM demo deployed on Google Cloud.

Use cases

• solutions to probabilistic robotics exercises
• learn how SLAM algorithms work
• FastSLAM implementation example
• SEIF sparse extended information filter implementation
• kalman filter and particle filter code examples
• study material for probabilistic robotics course
• run SLAM on the Victoria Park dataset

When to choose

• you are studying the 'Probabilistic Robotics' textbook and want worked exercise solutions
• you want reference implementations of SLAM algorithms like FastSLAM and SEIF
• you want to see Kalman filters, particle filters, and Bayesian inference applied to robotics in multiple languages

When to avoid

• you need a production-ready robotics middleware or SLAM library for a real robot
• you want a complete set of solutions - the project is a work in progress and not all exercises are covered
• you need a maintained framework with API stability rather than educational code

Facets

learning-resource · maturity active

simulation math developer-tools robotics autonomous-vehicles education mathematics cpp python cross-platform cloud probabilistic-robotics textbook-solutions kalman-filter particle-filter slam fastslam seif bayesian-inference victoria-park-dataset julia matlab armadillo lapack exercise-solutions algorithms

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