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.

An implementation of particle filtering algorithm for simultaneous localization and mapping (SLAM) in autonomous robots.

SLAM with occupancy grid and particle filter, using lidar, joints, IMU and odometry data from THOR humanoid robot

Using particle filter to fuse the wheel encoder, IMU and 2D lidar to localize the robot and create an occupancy grid map.

SLAM navigation on simplified scenario (FastSLAM implementation using Python) based on Particle Filter (Sequential Monte Carlo). What happens when the visual support of a drone is missing?

UCSD ECE 276A Winter 2020 Project 2: Particle Filter SLAM

Kalman, Particle and SLAM Filters implemented for the 2012/2013 Robotics exam.

A modular SLAM system combining Particle Filter-based localization, Occupancy Grid Mapping (OGM), Dynamic Window Approach (DWA) for real-time obstacle avoidance, and D* Lite for global path replanning. This project integrates both probabilistic mapping and real-time motion planning, suitable for research and educational use in robotics.

SLAM (Simultaneous Localization and Mapping): Position estimation of vehicle and obstacles with Extended-Kalman and Particle filters in Matlab, using the System Identification Toolbox.

Particle-filter based SLAM.

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particle filter based slam: fastslam1

Implementation of Simultaneous Localization and Mapping for a point featured map simulating a robot with Lidar measurments. Along the pre-determined trajectory, the point featured landmarks as well as an occupancy grid is sketched.

Localization -SLAM problem with Extended Kalman filter and Particle filter

Code for the Python EV3 Mindstorms platform. Slam, Line Follower, Particle filters and more

ESE 650: Learning in Robotics, Project 4, SLAM using Particle Filter and Occupancy Grid Mapping with 3D Map Generation

The exercises are all part of a typical application theme, namely tracking, navigation and SLAM: • Bayesian estimation applied to beacon based measurement systems • Kinematic and dynamic models for tracking • Tracking based on discrete Kalman filtering for linear-Gaussian systems • Tracking with extended Kalman filtering in nonlinear systems • Tracking with particle filtering in nonlinear systems • Slam As such the exercises cover the following theoretical subjects: 1. Fundamentals of parameter estimation; static and scalar case 2. Unbiased linear minimum mean square estimation; static and scalar case 3. Unbiased linear minimum mean square estimation; static and vectorial case 4. Propagation of uncertainty in Gaussian-linear systems; prediction 5. Discrete Kalman filtering 6. Extended Kalman filtering 7. Particle filtering 8. SLAM

Theory and python code for understanding autonomous navigation. This repo deals with Bayes, Kalman and Particle filters, SLAM.

Simultaneous Localization and Mapping (SLAM) in an indoor environment using information from an IMU and a LiDAR sensor collected from a humanoid robot called THOR.

framework for slam (EKF and particle filter, FastSLAM)

Implementation of LIDAR-based SLAM on 2-D grid map using particle filtering

Simulating a 2 wheel differential drive robot for implementing particle filter-based localization of the robot mobile robot by using a single lidar sensor under SLAM in ROS environment

CMU - Robotics: Localization and Mapping ( particle filter localization project )

The project was aimed at building a map along with simultaneous localization of the robot based on data from the encoder, fiber opto gyro and 2D lidar sensors. The localization was done by implementing a Particle Filter and the mapping by Occupancy Grid Mapping.

A particle filter solution for simultaneous localization and mapping written in Python

Simultaneous Localization and Mapping using Particle Filter

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Rao-Blackwellised Particle Filter SLAM without scan-matching of LIDAR measurements

This is a comprehensive project focused on implementing popular algorithms for state estimation, robot localization, 2D mapping, and 2D & 3D SLAM. It utilizes various types of filters, including the Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter.