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Rgbd mapping: Using depth cameras for dense 3d modeling of indoor environments (2010)

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by Peter Henry , Michael Krainin , Evan Herbst , Xiaofeng Ren , Dieter Fox
Venue:In RGB-D: Advanced Reasoning with Depth Cameras Workshop in conjunction with RSS
Citations:149 - 13 self
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BibTeX

@INPROCEEDINGS{Henry10rgbdmapping:,
    author = {Peter Henry and Michael Krainin and Evan Herbst and Xiaofeng Ren and Dieter Fox},
    title = {Rgbd mapping: Using depth cameras for dense 3d modeling of indoor environments},
    booktitle = {In RGB-D: Advanced Reasoning with Depth Cameras Workshop in conjunction with RSS},
    year = {2010}
}

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Abstract

Abstract RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras. 1

Keyphrases

indoor environment    rgbd mapping    using depth camera    depth information    rgb-d camera    view-based loop closure detection    capture rgb    consistent map    robot navigation    mapping system    shape information    large indoor environment    pose optimization    semantic mapping    per-pixel depth information    visual feature    novel joint optimization    shape-based alignment    abstract rgb-d camera    rgb-d mapping    present rgb-d mapping   

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