Indoor SLAM for Micro Aerial Vehicles Control Using Monocular Camera and Sensor Fusion

This paper represents research in progress in Simultaneous Localization and Mapping (SLAM) for Micro Aerial Vehicles (MAVs) in the context of rescue and/or recognition navigation tasks in indoor environments. In this kind of applications, the MAV must rely on its own onboard sensors to autonomously navigate in unknown, hostile and GPS denied environments, such as ruined or semi-demolished buildings. This article aims to investigate a SLAM technique that fuses visual information and measurements from the inertial measurement unit (IMU), to robustly obtain the 6DOF pose estimation of a MAV within a local map of the environment. The monocular visual SLAM algorithm along with the IMU calculate the pose estimation through an Extended Kalman Filter (EKF). The system consists of a low-cost commercial drone and a remote control unit to computationally afford the SLAM algorithms using a distributed node system based on ROS (Robot Operating System). Some experimental results show how sensor fusion improves the position estimation and the obtained map under different test conditions.