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RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments (2012)
| Venue: | The International Journal of Robotics Research |
| Citations: | 86 - 1 self |
BibTeX
@ARTICLE{Henry12rgb-dmapping:,
author = {Peter Henry and Michael Krainin and Evan Herbst and Xiaofeng Ren and Dieter Fox},
title = {RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments},
journal = {The International Journal of Robotics Research},
year = {2012},
pages = {647--663}
}
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Abstract
Abstract RGB-D cameras (such as the Microsoft Kinect) 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 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 rgb-d mapping using kinect-style depth camera depth information pose optimization view-based loop closure detection capture rgb microsoft kinect consistent map robot navigation mapping system shape information large indoor environment rgb-d camera semantic mapping per-pixel depth information visual feature novel joint optimization shape-based alignment abstract rgb-d camera present rgb-d mapping
