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• W3100402566 abstract "The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: 1) noise and outlier removal and 2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning." @default.
• W3100402566 created "2020-11-23" @default.
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• W3100402566 date "2018-01-01" @default.
• W3100402566 modified "2023-09-28" @default.
• W3100402566 title "Sparse 3D Point-Cloud Map Upsampling and Noise Removal as a vSLAM Post-Processing Step: Experimental Evaluation" @default.
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• W3100402566 doi "https://doi.org/10.1007/978-3-319-99582-3_3" @default.
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