A beginner-friendly SLAM mini-course with Jupyter notebooks — covering Bayes Filters, Kalman Filters, Particle Filters, and Graph-based SLAM with hands-on Python examples.

Creating Occupancy Grid Maps using Static State Bayes filter and Bresenham's algorithm for mobile robot (turtlebot3_burger) in ROS.

High-performance localization software for autonomous vehicles. A particle filter is combined with a map to localize a vehicle.

A basic C++ library for Bayes Filters.

Visual-Inertial SLAM performed with an RGBD and IMU dataset.

A Python package for (multiple) object tracking using recursive Bayesian filtering

2D particle filter in C++11.

This repository contains simple implementations for different Bayesian filters (Kalman, Extended Kalman, Unscented Kalman, and Particle filters)

Exploring RL algorithms like Value Iteration, Policy Iteration, Path Planning(RRT, PRM) etc. on an AI powered robot.

Particle Filter SLAM performed with a LIDAR and IMU dataset.

Implementation of an AI that plays the Pacman game using: Search agents, Minimax agents and a Bayes filter.

Kidnapped Vehicle (project 6 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

Differentiable Sequence Models

This is a C++ Uni Bonn course project for SoSe 25, which demonstrates the creation of a 3D occupancy grid map from a series of LiDAR scans taken from the 3D LiDAR sensor - Hesai XT-32, mounted on a ClearPath Husky robotic platform. It leverages modern C++17 features, the Eigen library for linear algebra ops and the Open3D library for visualisation

Reinforsment learning, dara-driven modeling, genetic algorithms

Crystal-clear Bayes filter implementation for robot localization. Watch probabilities evolve in real-time as a robot navigates with motion uncertainty. Perfect educational example of probabilistic robotics

🕹️ Basic AI algorithms from scratch for Pacman game

Provides a base class and tools for implementing a Bayes Histogram Filter.