The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels
Open accessAbstract
This letter presents an incremental voxel-based lidar-inertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. To achieve the high tracking speed, we neither use complicated tree-based structures to divide the spatial point cloud nor the strict k nearest neighbor (k-NN) queries to compute the point matching. Instead, we use the incremental voxels (iVox) as our point cloud spatial data structure, which is modified from the traditional voxels and supports incremental insertion and parallel approximated k-NN queries. We propose the linear iVox and PHC (Pseudo Hilbert Curve) iVox as two alternative underlying structures in our algorithm. The experiments show that the speed of iVox reaches 1000–2000 Hz per scan in solid-state lidars and over 200 Hz for 32 lines spinning lidars only with a modern CPU while still preserving the same level of accuracy.
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Citations
Cites
3D is here: Point Cloud Library (PCL)
Rusu, R. B., & Cousins, S. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 4,885
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
The R*-tree: an efficient and robust access method for points and rectangles
Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990) · Proceedings of the 1990 ACM SIGMOD international conference on Management of data · doi:10.1145/93597.98741 · Cited by 4,161
LOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303
Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain
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
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., & Rus, D. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341176 · Cited by 2,199
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
Self-citationFAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
Xu, W., & Zhang, F. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3064227 · Cited by 1,127
Self-citationReal-time 3D reconstruction at scale using voxel hashing
Nießner, M., Zollhöfer, M., Izadi, S., & Stamminger, M. (2013) · ACM Transactions on Graphics · doi:10.1145/2508363.2508374 · Cited by 1,002
University of Michigan North Campus long-term vision and lidar dataset
Carlevaris-Bianco, N., Ushani, A. K., & Eustice, R. M. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915614638 · Cited by 576
Metadata
- Authors
- Bai, C., Xiao, T., Chen, Y., Wang, H., Zhang, F., & Gao, X.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2022-04
- Volume
- 7
- Issue
- 2
- Pages
- 4861-4868
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W4292544413
- Cited by
- 389
- References
- 39
- FWCI
- 32.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Bai, C., Xiao, T., Chen, Y., Wang, H., Zhang, F., & Gao, X. (2022). Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2022.3152830
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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