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Micro Aerial Vehicles

Micro Aerial Vehicles (MAV)

At the chair of process automation, we have been working on autonomous systems for more than 15 years (UGVs, airship/blimp, automated driving). In 2007 we started working with quadrotors by using 3 AscTec Hummingbirds shortly after they became available. From the beginning, our research has focused on fully autonomous systems in indoor/GPS-denied environments without external sensors or computation. Since this required additional on-board sensors and computing power we got an AscTec Pelican quadrotor in 2010 because of its higher payload. The results of several projects using these MAVs with additional sensors and modifications have been published since 2009, e.g. an autonomous landing procedure by using a camera and a self-made optical flow sensor or in 2010, one of the first autonomous indoor flights using a Kinect on the Pelican.

Simulation of an overall system
We successfully participated in the first part of the European Robotics Challenges (EuRoC). As part of this challenge, we developed an autonomous multirotor system in simulation. The difficulty was to solve state estimation, localization, mapping and planning for a simulated system with restricted on-board processing.
Factor graph based sensor fusion
Higher-level navigation algorithms like path planning or mapping are depending on the performance of lower-level algorithms, especially the state estimation. For state estimation usually filter based algorithms like the Extended Kalman Filter (EKF) are used to combine the various sensors in a probabilistic way. However, as this solution has its drawbacks like linearization errors, the handling of delayed measurements or possible inconsistencies, we build our solution upon a factor graph based optimization algorithm.
System identification, filter and controller design
We developed a cost-effective motion capture system similar to motion capture systems, for pose measurement of a robot. The robot is equipped with active markers, which can be detected with a single camera using image processing algorithms. By solving a three-point-perspective-pose estimation problem, we can calculate the systems pose. [More details]
Autonomous flight using an RGB-D camera
With the introduction of the kinect camera, a cost-effective and easy-usable 3D sensor became available to the robotic community. We evaluated the sensor within the application of an autonomous corridor flight using the Pelican quadrotor. By using the kinect's point cloud measurements, a subsequent algorithm could find the planes for the corridor for additional position control input to hold the quadrotor in the middle of the corridor while flying. [More details]
Optical flow
We developed a micro-controller-based optical flow system for position stabilization of our quadrotor systems. By using an ADNS-3080 optical flow sensor as used in optical mice, we measure the velocity above ground, which can be converted to metric units by using an additional sonar sensor for altitude information. If the quadrotor is used indoors, this information is essential for position stabilized flight if the only other available source of information are the MAV's internal sensors. [More details]
Autonomous landing
In areas with poor GNSS-signals or in GNSS-denied environments it is hard to accomplish accurate autonomous landing procedures, so we developed a simple camera-based system for precision landing. Based on a predefined landing pad, an image processing algorithm on-board the MAV provides relative position information for a subsequent control algorithm. [More details]