Abstract
The RGBD sensors have opened the door to low cost perception capabilities for robots and to new approaches on the classic problems of self localization and environment mapping. The raw data coming from these sensors are typically huge clouds of 3D colored points, which are heavy to manage. This paper describes a premilinary work on an algorithm that incrementally builds compact and dense 3D maps of planar patches from the raw data of a mobile RGBD sensor. The algorithm runs iteratively and classifies the 3D points in the current sensor reading into three categories: close to an existing patch, already contained in one patch, and far from any. The first points update the corresponding patch definition, the last ones are clustered in new patches using RANSAC and SVD. A fusion step also merges 3D patches when needed. The algorithm has been experimentally validated in the Gazebo-5 simulator.
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Navarro, J., Cañas, J.M. (2016). Incremental Compact 3D Maps of Planar Patches from RGBD Points. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_51
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DOI: https://doi.org/10.1007/978-3-319-27149-1_51
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