Vision-based Autonomous Drone racing in GPS-denied Environments

High-speed autonomous flight of Micro Air Vehicles has gained much attention in recent years. However, flight in complex GPS-denied environments still poses a serious challenge. One scenario which contains these elements is drone racing, where pilots have to fly complex tracks at high speed, often in an indoor environment. In this work we...

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...

Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments

This thesis presents a visual route following method that minimizes memory consumption to the point that even Micro Aerial Vehicles (MAV) equipped with only a simple microcontroller can traverse distances of a few hundred meters. Existing Simultaneous Localization and Mapping (SLAM) algorithms are too complex for use on a microcontroller....

Stability-based Scale Estimation of Monocular SLAM for Autonomous Quadrotor Navigation

We propose a novel method to deal with the scale ambiguity in monocular SLAM based on control stability. We analytically show that (1) using unscaled state feedback from monocular SLAM for control can lead to system instability, and (2) there is a unique linear relationship between the absolute scale of the SLAM system and the control gain at...