The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An ICP variant using a point-to-line metric
Abstract
This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in Minguez et al. (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download.
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
Object modeling by registration of multiple range images
Chen, Y., & Medioni, G. (2002) · Proceedings. 1991 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1991.132043 · Cited by 1,409
Cited by
A Review of Point Cloud Registration Algorithms for Mobile Robotics
Pomerleau, F., Colas, F., & Siegwart, R. (2015) · Foundations and Trends® in Robotics · doi:10.1561/2300000035 · Cited by 717
Multiple-Robot Simultaneous Localization and Mapping: A Review
Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016) · Journal of Field Robotics · doi:10.1002/rob.21620 · Cited by 324
Yang, H., Antonante, P., Tzoumas, V., & Carlone, L. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2965893 · Cited by 325
A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles
Zou, Q., Sun, Q., Chen, L., Nie, B., & Li, Q. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3063477 · Cited by 321
A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
Wang, Y.-T., Peng, C.-C., Ravankar, A. A., & Ravankar, A. (2018) · Sensors · doi:10.3390/s18041294 · Cited by 84
Metadata
- Authors
- Censi, A.
- Venue
- 2008 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2008-05
- Pages
- 19-25
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2130763585
- Cited by
- 626
- References
- 1
- FWCI
- 15.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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: An ICP variant using a point-to-line metric. https://app.cochat.ai/papers/10.1109/robot.2008.4543181
- The paper
- Censi, A. (2008). An ICP variant using a point-to-line metric. 2008 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/robot.2008.4543181
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.