mirror of https://github.com/koide3/small_gicp.git
Add paper (#84)
* add paper.(md|bib) * Update paper.yml * fix references * revise paper * add missing DOI * consistent citation style * use CC and url commands
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name: Draft PDF
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on:
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push:
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branches: [ paper ]
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# Allows you to run this workflow manually from the Actions tab
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workflow_dispatch:
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jobs:
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paper:
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runs-on: ubuntu-latest
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name: Paper Draft
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Build draft PDF
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uses: openjournals/openjournals-draft-action@master
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with:
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journal: joss
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# This should be the path to the paper within your repo.
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paper-path: docs/paper.md
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- name: Upload
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uses: actions/upload-artifact@v3
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with:
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name: paper
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# This is the output path where Pandoc will write the compiled
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# PDF. Note, this should be the same directory as the input
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# paper.md
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path: docs/paper.pdf
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@InProceedings{Rusu,
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author = {Radu Bogdan Rusu and Steve Cousins},
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title = {{3D is here: Point Cloud Library (PCL)}},
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booktitle = {{IEEE International Conference on Robotics and Automation (ICRA2011)}},
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month = {May},
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year = {2011},
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address = {Shanghai, China},
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doi = {10.1109/ICRA.2011.5980567}
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}
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@article{Zhou,
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author = {Qian-Yi Zhou and Jaesik Park and Vladlen Koltun},
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title = {{Open3D}: {A} Modern Library for {3D} Data Processing},
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journal = {arXiv:1801.09847},
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year = {2018},
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doi = {10.48550/arXiv.1801.09847}
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}
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@ARTICLE{Bai,
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author={Bai, Chunge and Xiao, Tao and Chen, Yajie and Wang, Haoqian and Zhang, Fang and Gao, Xiang},
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journal={IEEE Robotics and Automation Letters},
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title={Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels},
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year={2022},
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volume={7},
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number={2},
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pages={4861-4868},
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doi={10.1109/LRA.2022.3152830}
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}
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@inproceedings{Koide,
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title = {Voxelized GICP for Fast and Accurate 3D Point Cloud Registration},
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author = {Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno},
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booktitle = {IEEE International Conference on Robotics and Automation (ICRA2021)},
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pages = {11054--11059},
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year = {2021},
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month = {May},
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doi = {10.1109/ICRA48506.2021.9560835}
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}
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@article{Zhang,
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title={Iterative point matching for registration of free-form curves and surfaces},
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author={Zhang, Zhengyou},
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journal={International journal of computer vision},
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volume={13},
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number={2},
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pages={119--152},
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year={1994},
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publisher={Springer},
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doi={10.1007/BF01427149}
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}
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@inproceedings{Segal,
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title={Generalized-{ICP}},
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author={Segal, Aleksandr and Haehnel, Dirk and Thrun, Sebastian},
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booktitle={Robotics: science and systems},
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volume={2},
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number={4},
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pages={435},
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year={2009},
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doi={10.15607/rss.2009.v.021}
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}
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@INPROCEEDINGS{Wang,
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author={Wang, Han and Wang, Chen and Xie, Lihua},
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booktitle={IEEE International Conference on Robotics and Automation (ICRA2020)},
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title={Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection},
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year={2020},
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pages={2095-2101},
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doi={10.1109/ICRA40945.2020.9196764}
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}
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@inproceedings{Izadinia,
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title={Scene recomposition by learning-based icp},
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author={Izadinia, Hamid and Seitz, Steven M},
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booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2020)},
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pages={930--939},
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year={2020},
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doi={10.1109/cvpr42600.2020.00101}
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}
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@ARTICLE{Kim,
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author={Kim, Kyuwon and Im, Junhyuck and Jee, Gyuin},
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journal={IEEE Transactions on Intelligent Transportation Systems},
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title={Tunnel Facility Based Vehicle Localization in Highway Tunnel Using 3D LIDAR},
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year={2022},
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volume={23},
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number={10},
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pages={17575-17583},
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doi={10.1109/TITS.2022.3160235}
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}
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@article{Pomerleau,
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author = {Pomerleau, Fran{\c c}ois and Colas, Francis and Siegwart, Roland and Magnenat, St{\'e}phane},
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title = {{Comparing ICP Variants on Real-World Data Sets}},
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journal = {Autonomous Robots},
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year = {2013},
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volume = {34},
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number = {3},
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pages = {133--148},
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month = feb,
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doi={10.1007/s10514-013-9327-2}
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}
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@INPROCEEDINGS{Serafin,
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author={Serafin, Jacopo and Grisetti, Giorgio},
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booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2015)},
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title={NICP: Dense normal based point cloud registration},
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year={2015},
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pages={742-749},
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doi={10.1109/IROS.2015.7353455}}
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@inproceedings{Datar,
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author = {Datar, Mayur and Immorlica, Nicole and Indyk, Piotr and Mirrokni, Vahab S.},
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title = {Locality-sensitive hashing scheme based on p-stable distributions},
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year = {2004},
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doi = {10.1145/997817.997857},
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booktitle = {Twentieth Annual Symposium on Computational Geometry},
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pages = {253–262},
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}
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@ -0,0 +1,127 @@
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---
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title: 'small_gicp: Efficient and parallel algorithms for point cloud registration'
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tags:
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- C++
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- Python
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- Point cloud registration
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authors:
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- name: Kenji Koide
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orcid: 0000-0001-5361-1428
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corresponding: true
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affiliation: "1"
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affiliations:
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- name: National Institute of Advanced Industrial Science and Technology (AIST), Japan
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index: 1
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date: 22 June 2024
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bibliography: paper.bib
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---
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\def\CC{{C\nolinebreak[4]\hspace{-.05em}\raisebox{.4ex}{\tiny\bf ++}}}
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# Summary
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Point cloud registration is a task of aligning two point clouds measured by 3D ranging
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sensors, for example, LiDARs and range cameras. Iterative point cloud registration,
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also known as fine registration or local registration, iteratively refines the transformation
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between point clouds starting from an initial guess.
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Each iteration involves a proximity-based point correspondence search and the minimization
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of the distance between corresponding points, continuing until convergence.
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Iterative closest point (ICP) and its variants,
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such as Generalized ICP, are representative iterative point cloud registration algorithms.
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They are widely used in applications like autonomous vehicle localization [@Kim], place recognition [@Wang],
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and object classification [@Izadinia]. Since these applications often require real-time or near-real-time
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processing, speed is a critical factor in point cloud registration routines.
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**small_gicp** provides efficient and parallel algorithms to create an extremely
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fast point cloud registration pipeline. It offers parallel implementations of
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downsampling, nearest neighbor search, local feature extraction, and registration
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to accelerate the entire process.
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small_gicp is implemented as a header-only \CC library with minimal dependencies
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to offer efficiency, portability, and customizability.
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# Statement of need
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There are several point cloud processing libraries, and PCL [@Rusu], Open3D
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[@Zhou], libpointmatcher [@Pomerleau] are commonly used in real-time applications
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owing to their performant implementations.
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Although they offer numerous functionalities, including those required for point cloud
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registration, they present several challenges for practical applications and scientific
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research.
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**Processing speed:**
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A typical point cloud registration pipeline includes processes such as downsampling,
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nearest neighbor search (e.g., KdTree construction), local feature estimation, and
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registration error minimization.
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PCL and Open3D support multi-threading only for parts of these processes (feature
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estimation and registration error minimization), with the remaining single-threaded
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parts often limiting the overall processing speed.
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Additionally, the multi-thread implementations in these libraries can have significant
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overheads, reducing scalability to many-core CPUs.
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These issues make it difficult to meet real-time processing requirements, especially on
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low-specification CPUs. It is also difficult to fully utilize the computational power
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of modern high-end CPUs.
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**Customizability:**
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Customizing the internal workings (e.g., replacing the registration cost function or
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changing the correspondence search method) of existing implementations is challenging
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due to hard-coded processes. This poses a significant hurdle for research and development,
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where testing new cost functions and search algorithms is essential.
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**small_gicp:**
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To address these issues and accelerate the development of point cloud registration-related systems,
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we designed small_gicp with the following features:
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- Fully parallelized point cloud preprocessing and registration algorithms with minimal overhead,
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offering up to 2x speed gain in single-threaded scenarios and better scalability in multi-core
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environments.
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- A modular and customizable framework using \CC templates, allowing easy customization of the
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algorithm's internal workings while maintaining efficiency.
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- A header-only \CC library implementation for easy integration into user projects, with Python bindings
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provided for collaborative use with other libraries (e.g., Open3D).
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# Functionalities
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**small_gicp** implements several preprocessing algorithms related to point cloud registration, and
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ICP variant algorithms (point-to-point ICP, point-to-plane ICP, and Generalized ICP based on
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distribution-to-distribution correspondence).
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- Downsampling
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- Voxelgrid sampling
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- Random sampling
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- Nearest neighbor search and point accumulation structures
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- KdTree
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- Linear iVox (supports incremental points insertion and LRU-cache-based voxel deletion) [@Bai]
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- Gaussian voxelmap (supports incremental points insertion and LRU-cache-based voxel deletion) [@Koide]
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- Registration error functions
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- Point-to-point ICP error [@Zhang]
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- Point-to-plane ICP error
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- Generalized ICP error [@Segal]
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- Robust kernels
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- Least squares optimizers
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- GaussNewton optimizer
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- LevenbergMarquardt optimizer
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# Benchmark results
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- Single-threaded and multi-threaded (6 threads) `small_gicp::voxelgrid_sampling` are approximately 1.3x and 3.2x faster than `pcl::VoxelGrid`, respectively.
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- Multi-threaded construction of `small_gicp::KdTree` can be up to 6x faster than that of `nanoflann`.
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- Single-threaded `small_gicp::GICP` is about 2.4x faster than `pcl::GICP`, with the multi-threaded version showing better scalability.
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More details can be found at \url{https://github.com/koide3/small_gicp/blob/master/BENCHMARK.md}.
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# Future work
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The efficiency of nearest neighbor search significantly impacts the overall performance of point cloud registration.
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While small_gicp currently offers efficient and parallel implementations of KdTree and voxelmap, which are general and useful in many situations,
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there are other nearest neighbor search methods that can be more efficient under mild assumptions about the point cloud measurement model
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(e.g., projective search [@Serafin]).
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We plan to implement these alternative neighbor search algorithms to further enhance the speed of the point cloud registration process.
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The design of small_gicp, where nearest neighbor search and pose optimization are decoupled, facilitates the easy integration of these new search algorithms.
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# Acknowledgements
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This work was supported in part by JSPS KAKENHI Grant Number 23K16979.
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# References
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