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Ground Segmentation for Landing
In order for UAVs to fly and especially land autonomously we need evaluate the safety of landing spots. Having semantic information about the scenery, eg. road, forest, roof, etc, enables the UAV to pick a safe landing spot.
In order for UAVs to fly and especially land autonomously we need evaluate the safety of landing spots. Having semantic information about the scenery, eg. road, forest, roof, etc, enables the UAV to pick a safe landing spot.
In order for UAVs to fly and especially land autonomously we need evaluate the safety of landing spots. Having semantic information about the scenery, eg. road, forest, roof, etc, enables the UAV to pick a safe landing spot.
The goal of this project is to use Machine Learning to segment the grounds scenery into a set of classes using either a monocular or stereo camera setup. Part of the project is the evaluation of existing data-sets for training, finding the optimal algorithm, as well as creating an efficient representation of the segmented areas, so that it can be integrated into a complete system.
The goal of this project is to use Machine Learning to segment the grounds scenery into a set of classes using either a monocular or stereo camera setup. Part of the project is the evaluation of existing data-sets for training, finding the optimal algorithm, as well as creating an efficient representation of the segmented areas, so that it can be integrated into a complete system.
- Kevin Kleber: kevinkleber@ifi.uzh.ch
- Kunal Shrivastava: shrivastava@ifi.uzh.ch
- Antonio Loquercio: loquercio@ifi.uzh.ch
- Kevin Kleber: kevinkleber@ifi.uzh.ch - Kunal Shrivastava: shrivastava@ifi.uzh.ch - Antonio Loquercio: loquercio@ifi.uzh.ch