Lukas Goormann

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This paper presents an approach to create three-dimensional occupancy maps from an aerial vehicle with stereo vision. The main idea is to create an occupancy grid that moves along with the vehicle and extract features into a fixed global map. Vice versa, global features or a-priori knowledge can be inserted into the grid. The maps are calculated onboard to(More)
This paper presents a mapping process that can be used for autonomous applications like obstacle avoidance and trajectory planning. The process is real-time capable, and works in full 3-D environments. The mapping starts with building an occupancy grid out of sensor data. Within this grid, single objects are recognized and their polygonal shapes are(More)
This paper presents an obstacle avoidance method that is performed with an unmanned helicopter. The approach begins with a mapping step where information from sensor data about previously unknown dangers is extracted into an occupancy grid and eventually converted into a polygonal 3D world model. This continuously updating map is used by a path planner that(More)
This work summarizes a multi-disciplinary research project, focusing on key enabling techniques towards true autonomous flight of small, low flying VTOL UAVs. Research activities cover the flying testbed, a simulation and testing environment, as well as integrated components for onboard navigation, perception, planning and control. Promising results and(More)
The challenge for unmanned aerial vehicles to sense and avoid obstacles becomes even harder if narrow passages have to be passed for which no precise a-priori position information is available. Inspired by recent UAV flight competitions, this work presents a vision-based approach to search for a narrow gate and to fly through it autonomously. The gate’s(More)
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