The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Voxelized GICP for Fast and Accurate 3D Point Cloud Registration
Abstract
This paper presents the voxelized generalized iterative closest point (VGICP) algorithm for fast and accurate three-dimensional point cloud registration. The proposed approach extends the generalized iterative closest point (GICP) approach with voxelization to avoid costly nearest neighbor search while retaining its accuracy. In contrast to the normal distributions transform (NDT), which calculates voxel distributions from point positions, we estimate voxel distributions by aggregating the distribution of each point in the voxel. The voxelization approach allows us to efficiently process the optimization in parallel, and the proposed algorithm can run at 30 Hz on a CPU and 120 Hz on a GPU. Through evaluations in simulated and real environments, we confirmed that the accuracy of the proposed algorithm is comparable to GICP, but is substantially faster than existing methods. This will enable the development of real-time 3D LIDAR applications that require extremely fast evaluations of the relative poses between LIDAR frames.
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Cites
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Shan, T., & Englot, B. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594299 · Cited by 2,295
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018) · Springer Proceedings in Advanced Robotics · doi:10.1007/978-3-319-67361-5_40 · Cited by 2,254
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Cited by
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
Bai, C., Xiao, T., Chen, Y., Wang, H., Zhang, F., & Gao, X. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3152830 · Cited by 389
Collaborative Dynamic 3D Scene Graphs for Automated Driving
Greve, E., Büchner, M., Vödisch, N., Burgard, W., & Valada, A. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610112 · Cited by 32
Metadata
- Authors
- Koide, K., Yokozuka, M., Oishi, S., & Banno, A.
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 11054-11059
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3007240855
- Cited by
- 386
- References
- 18
- FWCI
- 100.89
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Koide, K., Yokozuka, M., Oishi, S., & Banno, A. (2021). Voxelized GICP for Fast and Accurate 3D Point Cloud Registration. 2021 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra48506.2021.9560835
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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.
