Simultaneous Localization and Mapping(SLAM) also 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.
There is a large variety of SLAM (Simultaneous Localization and Mapping) approaches available in the robotics community. Throughout this work we focus on graph-based SLAM approach, a robust method for tracking an object over time and mapping out its surronding environment, using elements of probability, motion models and linear algebra.
The project is structured as a series of Jupyter notebooks that are written in Python and designed to be completed in sequential order:
robot_class.py
Notebook 1 : Robot Moving and Sensing;
Notebook 2 : Omega and Xi, Constraints;
Notebook 3 : Landmark Detection and Tracking.
Installation
$ git clone https://github.com/Noob-can-Compile/Landmark_Detection_Robot_Tracking_SLAM-.git
$ sudo pip3 install -r requirements.txt