This code is associated with the paper submitted to Encyclopedia of EEE titled: Robot localization: An Introduction

This repository contains C++ code for implementation of Particle Filter to localize a vehicle kidnapped in a closed environment. This task was implemented to partially fulfill Term-II goals of Udacity's self driving car nanodegree program.

Barelang FC Particle Filter Localization Simulation

Implementation of sensor fusion with the Marvelmind Indoor GPS ultrasonic beacons! (With custom message adapters and the Linorobot stack!)

Exercises based on the CV Nanodegree of Robot Localisation and implementations based on SLAM. Contains the techniques and filters in context to the same from scratch

An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! 🛰

A Python implementation of robot global localization in any 2-dimensional football field using histogram filter.

Robot Localization simulated on Gazebo using AMCL ROS Package with a Custom Robot and a Custom Gazebo world.

A modular software architecture for Automatic Plant Phenotyping

Unscented Kalman Filter using IMU and GNSS data for vehicle or mobile robot localization

EKF Localization Simulator - Educational Tool for EL2320 Applied Estimation at KTH Stockholm

Monte Carlo Localization Simulator - Educational Tool for EL2320 Applied Estimation at KTH Stockholm

This project builds a ROS-based Autonomous Robot from scratch

A code made for Artificial Intelligence classes to estimate the probability of the robot's localization.

Error State Kalman Filter All in One, from Theory to Practice.

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

Project for Artificial intelligence course @ Poznan University of Technology. The goal of the project was to localize robot using reinforcement learning

Particle Filter implementation : implement a particle filter and combine it with a real map to localize an object. Particle filter uses data and a map to determine the precise location of an object such as a robot or a vehicle.

Implement SLAM, a robust method for tracking an object over time and mapping out its surrounding environment using elements of probability, motion models, linear algerbra.

Find the implementation of Computer vision-based projects with Python, Deep Learning, and OpenCV