Exploring the unknown environment is a fundamental problem in the field of autonomous mobile robotics, which deals with exploring unknown areas while creating a map of the environment. Usually a human makes a map of the environment beforehand, and this map is used by the robot to navigate afterwards, avoiding obstacles. Boundary-based exploration is the most common approach, with the boundary acting as the place between open and unexplored areas. There are many applications of exploration algorithms in areas such as space robotics, sensor deployment, and protective robotics, etc. Along with this, many boundary-based methods have been developed, such as Wavefront Frontier Detector and Fast Frontier Detection, which reduce the temporal complexity of the original boundary-based exploration methodology. The simultaneous localization and mapping algorithm allows to work in unknown terrain and update the existing map, etc.

This paper describes and implements an autonomous boundary exploration strategy and presents simulation results of the simultaneous localization and mapping algorithm, in the Gazebo simulation environment, and on the TurtleBot hardware platform using the Robot Operating System. The advantage of this algorithm is that the robot can explore large open spaces as well as small cluttered spaces.