The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Robust odometry estimation for RGB-D cameras
Abstract
The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · cited 1×
“To achieve robustness against outliers many approaches use binary thresholding [21], [24] to segment the image into inliers and outliers, or continuous methods from robust statistics [25], [26].”
Method
Cited by
SVO: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., & Scaramuzza, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6906584 · Cited by 2,205
Robust visual inertial odometry using a direct EKF-based approach
Bloesch, M., Omari, S., Hutter, M., & Siegwart, R. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353389 · Cited by 917
Learning Depth from Monocular Videos Using Direct Methods
Wang, C., Buenaposada, J. M., Zhu, R., & Lucey, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00216 · Cited by 667
DeepTAM: Deep Tracking and Mapping
Zhou, H., Ummenhofer, B., & Brox, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_50 · Cited by 226
PROBE-GK: Predictive robust estimation using generalized kernels
Peretroukhin, V., Vega-Brown, W., Roy, N., & Kelly, J. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487212 · Cited by 17
Metadata
- Authors
- Kerl, C., Sturm, J., & Cremers, D.
- Venue
- 2013 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 3748-3754
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2021930164
- Cited by
- 542
- References
- 3
- FWCI
- 93.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kerl, C., Sturm, J., & Cremers, D. (2013). Robust odometry estimation for RGB-D cameras. 2013 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2013.6631104
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