The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
ICP registration using invariant features
Abstract
Investigates the use of Euclidean invariant features in a generalization of iterative closest point (ICP) registration of range images. Pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that, under ideal noise-free conditions, correspondences formed using this distance function are correct more often than correspondences formed using the positional distance alone. In addition, monotonic convergence to at least a local minimum is shown to hold for this method. When noise is present, a method that automatically sets the optimal relative contribution of features and positions is described. This method trades off the error in feature values due to noise against the error in positions due to misalignment. Experimental results suggest that using invariant features decreases the probability of being trapped in a local minimum and may be an effective solution for difficult range image registration problems where the scene is very small compared to the model.
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Cites
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FAST-LIO2: Fast Direct LiDAR-Inertial Odometry
Xu, W., Cai, Y., He, D., Lin, J., & Zhang, F. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3141876 · Cited by 1,750
Deformable medical image registration: a survey
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“[316] investigated the use of shape features in addition to the positional information when estimating the correspondences.”
BackgroundRGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911434148 · Cited by 1,161
Towards 3D Point cloud based object maps for household environments
Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.08.005 · Cited by 1,084
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
Tam, G. K. L., Cheng, Z.-Q., Lai, Y.-K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X.-F., & Rosin, P. L. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.310 · Cited by 679
Metadata
- Authors
- Sharp, G. C., Lee, S. W., & Wehe, D. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002
- Volume
- 24
- Issue
- 1
- Pages
- 90-102
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2144594079
- Cited by
- 614
- References
- 28
- FWCI
- 25.65
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
