Abstract
In this paper, an approach for a real-time three dimensional reconstruction of a dense point cloud, from images obtained with an unmanned aerial vehicle, is presented. The approach also estimates the model scale, using a known marker as reference, obtaining a fairly precise heightmap of the environment. The proposed image analysis framework is also capable of an off-line 3D reconstruction from monocular images, without known markers in the environment. The proposed framework is an adaptation of the Patch-based Multiview Stereo (PMVS) algorithm, in order to obtain a faster point cloud generation, suitable for real-time environment reconstruction. For the obtained heightmap, the error estimation ranges between 1–1.5 % for manual detection of marker and 4–5 % for automatic marker detection, which we consider is accurate enough for autonomous navigation and path planning.
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Acknowledgments
The authors would like to acknowledge the support of scholarship fund of CONACyT during A. Larranaga’s M.S. degree, and the support of the Cinvestav-LTI for the facilities to perform this work. We would also like to acknowledge the authors of the following libraries: OpenMVG, OpenCV, PMVS2 and Point Cloud Library.
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Ramirez-Torres, J.G., Larranaga-Cepeda, A. (2016). Real-Time Reconstruction of Heightmaps from Images Taken with an UAV. In: Zeghloul, S., Laribi, M., Gazeau, JP. (eds) Robotics and Mechatronics. Mechanisms and Machine Science, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-22368-1_22
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