OctoMap-based semi-autonomous quadcopter navigation with biosignal classification

In this paper, we propose a 3-D model based semi-autonomous navigation system with biosignal classification to control a quadcopter. Recently, some studies have proposed semi-autonomous navigation systems to resolve the inaccuracy of biosignal classification. However, these studies are based on 2-D models, which are inappropriate for 3-D real environments. This semi-autonomous navigation system resolves the limitations of the aforementioned papers by modeling the environment with an efficient 3-D model called OctoMap and uses this model to find a path that avoids obstacles. The performance of this proposed system was evaluated by comparing our system with the 2-D model based system mentioned above. This result shows the feasibility of our semi-autonomous system with OctoMap to control the quadcopter in 3-D space.