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Monocular Visual Mapping for Obstacle Avoidance on UAVs

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Abstract

An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Daniel Magree, John G. Mooney & Eric N. Johnson

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  1. Daniel Magree
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  2. John G. Mooney
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  3. Eric N. Johnson
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Correspondence to Daniel Magree.

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Magree, D., Mooney, J.G. & Johnson, E.N. Monocular Visual Mapping for Obstacle Avoidance on UAVs. J Intell Robot Syst 74, 17–26 (2014). https://doi.org/10.1007/s10846-013-9967-7

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  • DOI: https://doi.org/10.1007/s10846-013-9967-7

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