Project 1: Turtlebot3 SLAM (Simultaneous Localization and Mapping)

SLAM is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. There are several algorithms known for solving it, at least approximately, in tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, etc. 

The gmapping package contains a ROS wrapper for OpenSlam's Gmapping. The gmapping package provides laser-based SLAM, as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot.

The amcl (adaptive Monte Carlo localization) is a probabilistic localization system for a robot moving in 2D. It implements the adaptive Monte Carlo localization approach, which uses a particle filter to track the pose of a robot against a known map.

Please follow this tutorial to do the SLAM project.