Autonomous Mapping of Unknown Environments Using a UAV

Abstract

Automatic object search in a bounded area can be accomplished using cameracarrying autonomous aerial robots. The system requires several functionalities to solve the task in a safe and efficient way, including finding a navigation and exploration strategy, creating a representation of the surrounding environment and detecting objects visually. Here we create a modular framework and provide solutions to the different subproblems in a simulated environment. The navigation and exploration subproblems are tackled using deep reinforcement learning (DRL). Object and obstacle detection is approached using methods based on the scale-invariant feature transform and the pinhole camera model. Information gathered by the system is used to build a 3D voxel map. We further show that the object detection system is capable of detecting certain target objects with high recall. The DRL approach is able to achieve navigation that avoids collisions to a high degree, but the performance of the exploration policy is suboptimal. Due to the modular character of the solution further improvements of each subsystems can easily be developed independently.