The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Scan registration for autonomous mining vehicles using 3D‐NDT
Open accessAbstract
Scan registration is an essential subtask when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalization and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Strasser, which allows for accurate registration using a memory‐efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory‐efficient scan surface representation. © 2007 Wiley Periodicals, Inc.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
Object modelling by registration of multiple range images
Chen, Y., & Medioni, G. (1992) · Image and Vision Computing · doi:10.1016/0262-8856(92)90066-c · Cited by 3,154
The normal distributions transform: a new approach to laser scan matching
Biber, P., & Strasser, W. (2004) · Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453) · doi:10.1109/iros.2003.1249285 · Cited by 1,554
Geometrically stable sampling for the ICP algorithm
Gelfand, N., Ikemoto, L., Rusinkiewicz, S., & Levoy, M. (2004) · Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings. · doi:10.1109/im.2003.1240258 · Cited by 350
Registration of point cloud data from a geometric optimization perspective
Mitra, N. J., Gelfand, N., Pottmann, H., & Guibas, L. (2004) · Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing · doi:10.1145/1057432.1057435 · Cited by 262
A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping
Takeuchi, E., & Tsubouchi, T. (2006) · 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2006.282246 · Cited by 205
Cited by
Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark
Dong, Z., Liang, F., Yang, B., Xu, Y., Zang, Y., Li, J., Wang, Y., Dai, W., Fan, H., Hyyppä, J., et al. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.03.013 · Cited by 438
Li, L., Yang, F., Zhu, H., Li, D., Li, Y., & Tang, L. (2017) · Remote Sensing · doi:10.3390/rs9050433 · Cited by 283
Akai, N., Morales, L. Y., Takeuchi, E., Yoshihara, Y., & Ninomiya, Y. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995900 · Cited by 115
Metadata
- Authors
- Magnusson, M., Lilienthal, A., & Duckett, T.
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2007-10
- Volume
- 24
- Issue
- 10
- Pages
- 803-827
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2131439959
- Cited by
- 771
- References
- 8
- FWCI
- 20.48
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Scan registration for autonomous mining vehicles using 3D‐NDT. https://app.cochat.ai/papers/10.1002/rob.20204
- The paper
- Magnusson, M., Lilienthal, A., & Duckett, T. (2007). Scan registration for autonomous mining vehicles using 3D‐NDT. Journal of Field Robotics. https://doi.org/10.1002/rob.20204
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
