The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
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.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
A method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 18,103
Thrun, S. (2002) · Communications of the ACM · doi:10.1145/504729.504754 · Cited by 7,994
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
Closed-form solution of absolute orientation using unit quaternions
Horn, B. K. P. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.000629 · Cited by 4,116
Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988
Efficient variants of the ICP algorithm
Rusinkiewicz, S., & Levoy, M. (2002) · Proceedings Third International Conference on 3-D Digital Imaging and Modeling · doi:10.1109/im.2001.924423 · Cited by 3,945
Scalable Recognition with a Vocabulary Tree
Nister, D., & Stewenius, H. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.264 · Cited by 3,579
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995316 · Cited by 3,491
Cited by
KinectFusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092378 · Cited by 4,008
TEASER: Fast and Certifiable Point Cloud Registration
Yang, H., Shi, J., & Carlone, L. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3033695 · Cited by 895
CAD2RL: Real Single-Image Flight Without a Single Real Image
Sadeghi, F., & Levine, S. (2017) · Robotics: Science and Systems XIII · doi:10.15607/rss.2017.xiii.034 · Cited by 618
Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion
Keller, M., Lefloch, D., Lambers, M., Izadi, S., Weyrich, T., & Kolb, A. (2013) · 2013 International Conference on 3D Vision · doi:10.1109/3dv.2013.9 · Cited by 384
VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction
Innmann, M., Zollhöfer, M., Nießner, M., Theobalt, C., & Stamminger, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_22 · Cited by 308
Metadata
- Authors
- Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2012-02-10
- Volume
- 31
- Issue
- 5
- Pages
- 647-663
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W1972671602
- Cited by
- 1,161
- References
- 51
- FWCI
- 208.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D. (2012). RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments. The International Journal of Robotics Research. https://doi.org/10.1177/0278364911434148
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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