3D Mapping with an RGB-D Camera

3D Mapping with an RGB-D Camera. Endres, F., Hess, J., Sturm, J., Cremers, D., & Burgard, W. IEEE TRANSACTIONS ON ROBOTICS, 2014.

Paper abstract bibtex

In this article we present a novel mapping system that robustly generates highly accurate 3D maps using an RGB-D camera. Our approach does not require any further sensors or odometry. With the availability of low-cost and lightweight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners as well as flying robots such as quadrocopters. Furthermore, our system can also be used for free-hand reconstruction of detailed 3D models. In addition to the system itself, we present a thorough experimental evaluation on a publicly available benchmark dataset. We analyze and discuss the influence of several parameters such as the choice of the feature descriptor, the number of visual features, and validation methods. The results of the experiments demonstrate that our system can robustly deal with challenging scenarios such as fast cameras motions and feature-poor environments while being fast enough for online operation. Our system is fully available as open-source and has already been widely adopted by the robotics community.

@article{
 title = {3D Mapping with an RGB-D Camera},
 type = {article},
 year = {2014},
 keywords = {Index Terms-RGB-D,Localization,Mapping,Open-Source,SLAM},
 volume = {30},
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 abstract = {In this article we present a novel mapping system that robustly generates highly accurate 3D maps using an RGB-D camera. Our approach does not require any further sensors or odometry. With the availability of low-cost and lightweight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners as well as flying robots such as quadrocopters. Furthermore, our system can also be used for free-hand reconstruction of detailed 3D models. In addition to the system itself, we present a thorough experimental evaluation on a publicly available benchmark dataset. We analyze and discuss the influence of several parameters such as the choice of the feature descriptor, the number of visual features, and validation methods. The results of the experiments demonstrate that our system can robustly deal with challenging scenarios such as fast cameras motions and feature-poor environments while being fast enough for online operation. Our system is fully available as open-source and has already been widely adopted by the robotics community.},
 bibtype = {article},
 author = {Endres, Felix and Hess, Jürgen and Sturm, Jürgen and Cremers, Daniel and Burgard, Wolfram},
 journal = {IEEE TRANSACTIONS ON ROBOTICS},
 number = {1}
}

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