Effective robot navigation is crucial for the proper implementation of a mobile robot. To navigate a robot in an indoor or outdoor environment, a map of the surrounding obstacles is required. In order to acquire this map, the robot uses sensors to collect information about its surroundings and its own location. This paper presents a robotic operating system based on autonomous simultaneous localization and mapping (SLAM), and robot navigation implementation of a Parrot AR. Drone 2.0 quadrotor, which is equipped with a laser scanner and inertial measurement unit. For autonomous mapping, SLAM along with frontier type exploration has been used to acquire a full 2D map of the environment in Gazebo. The A* search algorithm is utilized for the global planner, while the dynamic window approach has been implemented for the local planner. The Gazebo-simulated results validate the competent performance of the underlying structure of both the proposed SLAM as well as the provided navigation algorithm.
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Alborzi et al. (2020) studied this question.
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