SLAM
SLAM (Simultaneous Localization and Mapping) is a technique used in robotics to build a map of an unknown environment while simultaneously keeping track of the robot’s location within it. This is a key technology for autonomous robots that need to operate in unknown environments.
Loop Closure
Loop Closure is the process by which a robot recognizes that it has returned to a previously visited location in its environment, even after traveling a significant distance or through complex paths. Detecting loop closure is essential for reducing drift in the robot’s estimated position and improving the accuracy of the map it is building.
Imagine being blindfolded, and then transported at the back of a car with armed kidnappers, only to be released near the Eiffel Tower. Suddenly, you realize “Hey, I know this place!” and you’re (somewhat) relieved [Source].
Loop Closure. Source
Libraries and ROS packages
SLAM Toolbox is a set of tools and capabilities for 2D SLAM GitHub It is also the currently supported ROS2-SLAM library. See tutorials for working with it in ROS 2 Nav2 here.
RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach. GitHub
nav_msgs/OccupancyGrid - represents a 2-D grid map, in which each cell represents the probability of occupancy
GMapping (G means grid because this algorithm uses a grid map) is a highly efficient Rao-Blackwellized particle filter to learn grid maps from laser range data GitHub