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

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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:	148 - 13 self

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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