Simultaneous Localisation and Mapping(SLAM) is a Robust method for tracking an object over time and mapping its sorroundings using elements of Probablilty ,Motion Models and Linear Algebra.
This project focuses on implementing a GRAPH SLAM for a 2 dimensional world by combining the robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time.SLAM gives you a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features. This is an active area of research in the fields of robotics and autonomous systems.
Below is an example of a 2D robot world with landmarks (purple x's) and the robot (a red 'o') located and found using only sensor and motion data collected by that robot. This is just one example for a 50x50 grid world; This project will likely able to generate a variety of these maps.
Download the project materials from the GitHub repository by using the download option or clone the github repository.After downloading or cloning navigate to the home folder of the project.
git clone https://github.com/chaithanya21/SLAM.git
cd SLAM
jupyter notebook