RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments

  • Authors:

  • Peter Henry;Michael Krainin;Evan Herbst;Xiaofeng Ren;Dieter Fox

  • Affiliations:

  • Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA;Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA;Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA;ISTC-Pervasive Computing, Intel Labs, Seattle, WA, USA;Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2012

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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.