  Limited power and computational resources, absence of high-end sensor
equipment and GPS-denied environments are challenges faced by autonomous micro
areal vehicles (MAVs). We address these challenges in the context of autonomous
navigation and landing of MAVs in indoor environments and propose a
vision-based control approach using Supervised Learning. To achieve this, we
collected data samples in a simulation environment which were labelled
according to the optimal control command determined by a path planning
algorithm. Based on these data samples, we trained a Convolutional Neural
Network (CNN) that maps low resolution image and sensor input to high-level
control commands. We have observed promising results in both obstructed and
non-obstructed simulation environments, showing that our model is capable of
successfully navigating a MAV towards a landing platform. Our approach requires
shorter training times than similar Reinforcement Learning approaches and can
potentially overcome the limitations of manual data collection faced by
comparable Supervised Learning approaches.
