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Abstract
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| This paper presents a fast and robust approach for estimating the flight altitude of multi-rotor UAVs using 3D point cloud sensors in cluttered, unstructured and dynamic environments. The objective is to present a complete sensor suite, replacing the conventional sensors such as laser altimeters, barometers or accelerometers, which have several limitations when using them individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data, along with the segmentation of the clustered data into horizontal planes is performed. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several ground obstacles. Our results have been validated with standard datasets that provide 3D point clouds generated by RGB-D cameras, available in the literature. We have further validated our approach, by means of several autonomous real flights, closing the altitude control loop using the estimated flight altitude obtained by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open source software framework for aerial robotics called Aerostac | |
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International
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JCR
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Title
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Journal of Intelligent & Robotic Systems |
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ISBN
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0921-0296 |
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Impact factor JCR
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1,512 |
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Impact info
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Datos JCR del año 2017 |
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