# pierrepault/probabilistic_robotics

solution of exercises of the book "probabilistic robotics"

Repository: https://github.com/pierrepault/probabilistic_robotics
Canonical: https://ross.abutalabs.com/products/probabilistic_robotics
Language: C++
License: MIT
License Family: permissive
Topics: probabilistic-robotics, bayesian-inference, kalman-filter, slam, navigation, particle-filter, julia, matlab, numpy, cpp, armadillo-library, lapack, intel-mkl-library, victoria-park-dataset, fastslam, seif, robotics, autonomous-vehicles, google-cloud-platform, textbook-solutions
Last push: 2023-05-18T06:43:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3037, "days_push": 1203, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1318, forks 389 (observed 2026-08-28T04:04:21.165966+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: simulation, math, developer-tools
- domain: robotics, autonomous-vehicles, education, mathematics
- platform: cpp, python, cross-platform, cloud
- tags: probabilistic-robotics, textbook-solutions, kalman-filter, particle-filter, slam, fastslam, seif, bayesian-inference, victoria-park-dataset, julia, matlab, armadillo, lapack, exercise-solutions, algorithms

## Member repositories
- pierrepault/probabilistic_robotics (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:21.165966+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:48:09.889544+00:00, confidence not recorded.
  - readme: https://github.com/pierrepault/probabilistic_robotics (fetched 2026-08-28T04:04:21.165966+00:00, sha 743ea9443419)
- Data as of 2026-08-30T08:39:29.467469+00:00.