Visual Heading Estimation for UAVs in Indoor Environments

Abstract

Recently, The UAV (Unmanned Aerial Vehicle) industry is getting a lot of attention, especially for very small drones that could fly indoors. Such a small drone can help perform rescue tasks such as investigating gas leaks or emergency situation that imposes risk on human intervention. Additionally, the UAVs can be utilized for indoor inspection and 3D mapping. The indoor environment is very challenging for the UAVs since the Global Navigation Satellite System (GNSS) cannot be reliably accessed to help UAV to locate itself. Various sensors could be mounted on UAVs to help the surveillance and navigation aspects of the operation. Range sensors such as 2D Lasers and LIDAR are very useful for providing valuable measurements towards reliable localization algorithms. Including such sensors will typically raise the system cost and limit the flight time due to increased power consumption. This research aims to assess the potential of using the typically installed UAV main camera to help estimate the UAV heading. The typical indoor environment includes many challenging situations such as monochrome surfaces and identical repeated patterns, especially in the ground surface. The research investigates the performance of different heading estimation approaches using a minimum cost configuration (without laser scanners). The proposed integration between the drone forward camera and the downward camera enhanced the navigation result compared to the other individual solutions using the downward camera only, forward camera only, or magnetometer. The performance of the investigated approaches in a real indoor flight is presented and discussed.