Deep Reinforcement Learning for Goal-directed Visual Navigation

Safe navigation in a cluttered environment is a key capability for the autonomous operation of Micro Aerial Vehicles (MAVs). This work explores a (deep) Reinforcement Learning (RL) based approach for monocular vision based obstacle avoidance and goal directed navigation for MAVs in cluttered environments. We investigated this problem in the...

Enhanced Sparse Depth Reconstruction Using Edge and Temporal Information: An Application to Micro Air Vehicles

The reconstruction of dense depth maps is of great value to resource-constrained Mirco Air Vehicles (MAVs), in the pursuit of achieving autonomous flight with a high situational awareness. Most MAVs implement sensing methods which provide a sparse depth map, limiting their capabilities significantly. This article introduces two novel methods to...