. However, this set of landmarks (rendering the map) is often too sparse for tasks in autonomous driving such as navigation, path planning, obstacle avoidance etc. Some methods keep the raw measurements for past robot poses to address the sparsity problem often resulting in a pose only SLAM akin to laser

terrain and potential obstacles. In this paper we show a new solution for a real-time dense 3D terrain reconstruction. This can be used for efficient unmanned MAV terrain exploration and yields a solid base for standard autonomous obstacle avoidance algorithms and path planners. Our approach is based on a

Abstract — This paper extends a monocular visual simultane-ous localization and mapping (SLAM) system to utilize two cameras with non-overlap in their respective field of views (FOVs). We achieve using it to enable autonomous navigation of a micro aerial vehicle (MAV) in unknown environments

Abstract — Highly accurate localization of a micro aerial vehicle (MAV) with respect to a scene is important for a wide range of applications, in particular surveillance and inspection. Most existing approaches to visual localization focus on indoor environments, while such tasks require outdoor

(MAV) capable of autonomous indoor navigation. The MAV is equipped with four cameras arranged in two stereo configu-rations. One camera pair is facing forward and serves as input for a reduced stereo SLAM system. The other camera pair is facing downwards and is used for ground plane detection

Abstract. Automatic obstacle detection and avoidance is a key component for the success of micro-aerial vehicles (MAVs) in the future. As the payload of MAVs is highly constrained, cameras are attractive sensors because they are both lightweight and provide rich information about the environment

Abstract — This paper presents a Miniature Aerial Vehicle (MAV) capable of hands-off autonomous operation within indoor environments. Our prototype is a Quadrotor weighing approximately 600g, with a diameter of 550mm, which carries the necessary electronics for stability control, altitude control

sparse stereo match-ing algorithm in combination with a visual odometry method based on PTAM to estimate the current MAV pose, which we require for autonomous control. All processing is performed on a single board computer on-board the MAV. To our knowledge, this is the first MAV that uses stereo vision

inertial sensors. Second, as our system is based on monocular vision, we present a solution to estimate the metric visual scale aid of an air pressure sensor. All computation is running onboard and is tightly integrated on the MAV to avoid jitter and latencies. This framework enables stable flights indoors

with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed