The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
A Review of Point Cloud Registration Algorithms for Mobile Robotics
Open accessAbstract
The topic of this review is geometric registration in robotics. Registration algorithms associate sets of data into a common coordinate system. They have been used extensively in object reconstruction, inspection, medical application, and localization of mobile robotics. We focus on mobile robotics applications in which point clouds are to be registered. While the underlying principle of those algorithms is simple, many variations have been proposed for many different applications. In this review, we give a historical perspective of the registration problem and show that the plethora of solutions can be organized and differentiated according to a few elements. Accordingly, we present a formalization of geometric registration and cast algorithms proposed in the literature into this framework. Finally, we review a few applications of this framework in mobile robotics that cover different kinds of platforms, environments, and tasks. These examples allow us to study the specific requirements of each use case and the necessary configuration choices leading to the registration implementation. Ultimately, the objective of this review is to provide guidelines for the choice of geometric registration configuration.
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Deep Closest Point: Learning Representations for Point Cloud Registration
Wang, Y., & Solomon, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00362 · Cited by 1,112
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
LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation
Qin, C., Ye, H., Pranata, C. E., Han, J., Zhang, S., & Liu, M. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197567 · Cited by 391
The current state and future outlook of rescue robotics
Delmerico, J., Mintchev, S., Giusti, A., Gromov, B., Melo, K., Horvat, T., Cadena, C., Hutter, M., Ijspeert, A., Floreano, D., et al. (2019) · Journal of Field Robotics · doi:10.1002/rob.21887 · Cited by 371
Self-citationCT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
Dellenbach, P., Deschaud, J.-E., Jacquet, B., & Goulette, F. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811849 · Cited by 280
DeepGMR: Learning Latent Gaussian Mixture Models for Registration
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Introspective Perception for Long-Term Aerial Telemanipulation With Virtual Reality
Lee, J., Balachandran, R., Kondak, K., Coelho, A., Stefano, M. D., Humt, M., Feng, J., Asfour, T., & Triebel, R. (2024) · IEEE Transactions on Field Robotics · doi:10.55417/fr.2023010 · Cited by 8
Metadata
- Authors
- Pomerleau, F., Colas, F., & Siegwart, R.
- Venue
- Foundations and Trends® in Robotics
- Publisher
- Emerald
- Published
- 2015-05-27
- Volume
- 4
- Issue
- 1
- Pages
- 1-104
- ISSN
- 1935-8253 (ISSN-L)
- 1935-8261
- OpenAlex
- W2271206385
- Cited by
- 717
- References
- 86
- FWCI
- 46.61
- Type
- journal article
- Language
- en
- Open access
- green
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