Exploration and mapping of an indoor environment using Multirotor Aerial Vehicle

Abstract

Self-exploration of unknown environments using mobile robots is an important aspect in order to achieve complete automation of the tasks done by robots. The key idea of the exploration is to find a pose at which the robot will gain more information about the environment. By moving from such a pose to another the robot builds a map using the information gained during motion. A frontier based exploration algorithm for 3D spaces is introduced. The algorithm is an extension of the classic frontier exploration algorithm. The space is represented in an octree structure for faster access and efficient memory management. At every iteration of the exploration algorithm, the Multirotor Aerial Vehicle (MAV) performs a 360° rotation to increase the information gain about the environment, this rotation is dependent on the sensor’s field of view. If a rotating sensor which can cover a 360° of the environment is used, the 360° rotation can be skipped without affecting the methodology. The distance moved by the MAV is specified at each iteration of the exploration process depending on the environment. Simulations were conducted to compare the effect of different parameters in the algorithm.