An efficient ground segmentation approach for LiDAR point cloud utilizing adjacent grids
Ground segmentation is crucial for guiding mobile robots and identifying nearby objects. However, it should be noted that the ground often presents complex topographical features, such as slopes and rugged terrains, which significantly increase the challenges associated with accurate ground segmenta...
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| Published in | Machine vision and applications Vol. 35; no. 5; p. 108 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0932-8092 1432-1769 |
| DOI | 10.1007/s00138-024-01593-5 |
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| Abstract | Ground segmentation is crucial for guiding mobile robots and identifying nearby objects. However, it should be noted that the ground often presents complex topographical features, such as slopes and rugged terrains, which significantly increase the challenges associated with accurate ground segmentation tasks. To address this issue, we propose a novel approach to achieve rapid ground segmentation. The proposed method uses a multi-partition approach to extract ground points for each partition, followed by assessing the correction plane based on geometric characteristics of the ground surface and similarity among adjacent planes. An adaptive threshold is also introduced to enhance efficiency in extracting complex urban pavement. Our method was benchmarked against several contemporary techniques on the SemanticKITTI dataset. The precision was elevated by 1.72
%
, and the precision deviation was diminished by 1.02
%
, culminating in the most accurate and robust outcomes among the evaluated methods. |
|---|---|
| AbstractList | Ground segmentation is crucial for guiding mobile robots and identifying nearby objects. However, it should be noted that the ground often presents complex topographical features, such as slopes and rugged terrains, which significantly increase the challenges associated with accurate ground segmentation tasks. To address this issue, we propose a novel approach to achieve rapid ground segmentation. The proposed method uses a multi-partition approach to extract ground points for each partition, followed by assessing the correction plane based on geometric characteristics of the ground surface and similarity among adjacent planes. An adaptive threshold is also introduced to enhance efficiency in extracting complex urban pavement. Our method was benchmarked against several contemporary techniques on the SemanticKITTI dataset. The precision was elevated by 1.72%, and the precision deviation was diminished by 1.02%, culminating in the most accurate and robust outcomes among the evaluated methods. Ground segmentation is crucial for guiding mobile robots and identifying nearby objects. However, it should be noted that the ground often presents complex topographical features, such as slopes and rugged terrains, which significantly increase the challenges associated with accurate ground segmentation tasks. To address this issue, we propose a novel approach to achieve rapid ground segmentation. The proposed method uses a multi-partition approach to extract ground points for each partition, followed by assessing the correction plane based on geometric characteristics of the ground surface and similarity among adjacent planes. An adaptive threshold is also introduced to enhance efficiency in extracting complex urban pavement. Our method was benchmarked against several contemporary techniques on the SemanticKITTI dataset. The precision was elevated by 1.72 % , and the precision deviation was diminished by 1.02 % , culminating in the most accurate and robust outcomes among the evaluated methods. |
| ArticleNumber | 108 |
| Author | Park, Bongrae Dong, Longyu Liu, Dejun Dong, Youqiang Wan, Zhibo |
| Author_xml | – sequence: 1 givenname: Longyu surname: Dong fullname: Dong, Longyu organization: College of Computer Science and Technology, Qingdao University – sequence: 2 givenname: Dejun surname: Liu fullname: Liu, Dejun organization: China Academy of Railway Sciences – sequence: 3 givenname: Youqiang surname: Dong fullname: Dong, Youqiang organization: Qingdao Haily Measuring Technologies Co., Ltd – sequence: 4 givenname: Bongrae surname: Park fullname: Park, Bongrae organization: Wapa System – sequence: 5 givenname: Zhibo surname: Wan fullname: Wan, Zhibo email: wanzhibo@qdu.edu.cn organization: College of Computer Science and Technology, Qingdao University |
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| Cites_doi | 10.3390/rs13163239 10.1109/IROS.2018.8594299 10.1109/IROS45743.2020.9340979 10.1109/ICRA48506.2021.9561364 10.1109/ITSC.2018.8569534 10.1109/IROS47612.2022.9981561 10.1109/ICCV48922.2021.01572 10.1109/ICCV.2019.00939 10.1109/JSEN.2022.3225293 10.1109/LRA.2022.3182096 10.1109/UR55393.2022.9826238 10.1109/TIV.2022.3187008 10.1145/358669.358692 10.1109/ICRA.2017.7989591 10.1109/ACCESS.2021.3115664 10.1109/LRA.2021.3061363 10.1109/LRA.2022.3201689 10.1109/ITSC.2015.133 10.1109/LRA.2021.3093009 10.1109/TRO.2020.3033695 10.1109/IVS.2010.5548059 10.1177/02783649231207654 10.1109/ICRA.2011.5979818 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Mapping Autonomous driving Ground segmentation Field robots |
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IEEE (2020) ShenZLiangHLinLWangZHuangWYuJFast ground segmentation for 3d lidar point cloud based on jump-convolution-processRemote Sens.20211316323910.3390/rs13163239 QianYWangXChenZWangCYangMHy-seg: a hybrid method for ground segmentation using point cloudsIEEE Trans. Intell. Veh.2022821597160610.1109/TIV.2022.3187008 Pan, Y., Xiao, P., He, Y., Shao, Z., Li, Z.: Mulls: Versatile lidar slam via multi-metric linear least square. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 11633–11640. IEEE (2021) Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017) Lee, S., Lim, H., Myung, H.: Patchwork++: fast and robust ground segmentation solving partial under-segmentation using 3d point cloud. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 13276–13283. IEEE (2022) OhMJungELimHSongWHuSLeeEMParkJKimJLeeJMyungHTravel: traversable ground and above-ground object segmentation using graph representation of 3d lidar scansIEEE Robot. Autom. Lett.2022737255726210.1109/LRA.2022.3182096 Zermas, D., Izzat, I., Papanikolopoulos, N.: Fast segmentation of 3d point clouds: a paradigm on lidar data for autonomous vehicle applications. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5067–5073. IEEE (2017) Yang, H., Shi, J., Carlone, L.: TEASER: fast and certifiable point cloud registration. IEEE Trans. Robot. (2020) Himmelsbach, M., Hundelshausen, F.V., Wuensche, H.-J.: Fast segmentation of 3d point clouds for ground vehicles. In: 2010 IEEE Intelligent Vehicles Symposium, pp. 560–565. IEEE (2010) Narksri, P., Takeuchi, E., Ninomiya, Y., Morales, Y., Akai, N., Kawaguchi, N.: A slope-robust cascaded ground segmentation in 3d point cloud for autonomous vehicles. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 497–504. IEEE (2018) JiménezVGodoyJArtuñedoAVillagraJGround segmentation algorithm for sloped terrain and sparse lidar point cloudIEEE Access2021913291413292710.1109/ACCESS.2021.3115664 Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: Semantickitti: a dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9297–9307 (2019) LimHOhMMyungHPatchwork: concentric zone-based region-wise ground segmentation with ground likelihood estimation using a 3d lidar sensorIEEE Robot. Autom. Lett.2021646458646510.1109/LRA.2021.3093009 Asvadi, A., Peixoto, P., Nunes, U.: Detection and tracking of moving objects using 2.5 d motion grids. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 788–793. IEEE (2015) LimHHwangSMyungHErasor: egocentric ratio of pseudo occupancy-based dynamic object removal for static 3d point cloud map buildingIEEE Robot. Autom. Lett.2021622272227910.1109/LRA.2021.3061363 Seo, D.-U., Lim, H., Lee, S., Myung, H.: Pago-loam: robust ground-optimized lidar odometry. In: 2022 19th International Conference on Ubiquitous Robots (UR), pp. 1–7. IEEE (2022) WangZYangLGaoFWangLFevo-loam: feature extraction and vertical optimized lidar odometry and mappingIEEE Robot. Autom. Lett.202274120861209310.1109/LRA.2022.3201689 Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., Frenkel, A.: On the segmentation of 3d lidar point clouds. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2798–2805. IEEE (2011) Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., Pu, S.: Rpvnet: a deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16024–16033 (2021) GuoDYangGQiBWangCA fast ground segmentation method of lidar point cloud from coarse-to-fineIEEE Sens. J.20222321357136710.1109/JSEN.2022.3225293 1593_CR2 Z Shen (1593_CR7) 2021; 13 1593_CR1 1593_CR6 1593_CR19 1593_CR4 1593_CR16 1593_CR8 1593_CR9 1593_CR18 1593_CR11 Z Wang (1593_CR21) 2022; 7 M Oh (1593_CR17) 2022; 7 1593_CR23 1593_CR24 V Jiménez (1593_CR14) 2021; 9 1593_CR20 1593_CR10 D Guo (1593_CR5) 2022; 23 H Lim (1593_CR22) 2024; 43 MA Fischler (1593_CR12) 1981; 24 H Lim (1593_CR13) 2021; 6 Y Qian (1593_CR15) 2022; 8 H Lim (1593_CR3) 2021; 6 |
| References_xml | – reference: QianYWangXChenZWangCYangMHy-seg: a hybrid method for ground segmentation using point cloudsIEEE Trans. Intell. Veh.2022821597160610.1109/TIV.2022.3187008 – reference: Asvadi, A., Peixoto, P., Nunes, U.: Detection and tracking of moving objects using 2.5 d motion grids. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 788–793. IEEE (2015) – reference: GuoDYangGQiBWangCA fast ground segmentation method of lidar point cloud from coarse-to-fineIEEE Sens. J.20222321357136710.1109/JSEN.2022.3225293 – reference: Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017) – reference: Lee, S., Lim, H., Myung, H.: Patchwork++: fast and robust ground segmentation solving partial under-segmentation using 3d point cloud. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 13276–13283. IEEE (2022) – reference: Pan, Y., Xiao, P., He, Y., Shao, Z., Li, Z.: Mulls: Versatile lidar slam via multi-metric linear least square. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 11633–11640. IEEE (2021) – reference: OhMJungELimHSongWHuSLeeEMParkJKimJLeeJMyungHTravel: traversable ground and above-ground object segmentation using graph representation of 3d lidar scansIEEE Robot. Autom. Lett.2022737255726210.1109/LRA.2022.3182096 – reference: LimHHwangSMyungHErasor: egocentric ratio of pseudo occupancy-based dynamic object removal for static 3d point cloud map buildingIEEE Robot. Autom. Lett.2021622272227910.1109/LRA.2021.3061363 – reference: Seo, D.-U., Lim, H., Lee, S., Myung, H.: Pago-loam: robust ground-optimized lidar odometry. In: 2022 19th International Conference on Ubiquitous Robots (UR), pp. 1–7. IEEE (2022) – reference: Zermas, D., Izzat, I., Papanikolopoulos, N.: Fast segmentation of 3d point clouds: a paradigm on lidar data for autonomous vehicle applications. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5067–5073. IEEE (2017) – reference: Shan, T., Englot, B.: Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4758–4765. IEEE (2018) – reference: LimHOhMMyungHPatchwork: concentric zone-based region-wise ground segmentation with ground likelihood estimation using a 3d lidar sensorIEEE Robot. Autom. Lett.2021646458646510.1109/LRA.2021.3093009 – reference: LimHKimBKimDMason LeeEMyungHQuatro++: robust global registration exploiting ground segmentation for loop closing in lidar slamInt. J. Robot. Res.202443568571510.1177/02783649231207654 – reference: Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., Pu, S.: Rpvnet: a deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16024–16033 (2021) – reference: Himmelsbach, M., Hundelshausen, F.V., Wuensche, H.-J.: Fast segmentation of 3d point clouds for ground vehicles. In: 2010 IEEE Intelligent Vehicles Symposium, pp. 560–565. IEEE (2010) – reference: WangZYangLGaoFWangLFevo-loam: feature extraction and vertical optimized lidar odometry and mappingIEEE Robot. Autom. Lett.202274120861209310.1109/LRA.2022.3201689 – reference: Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., Frenkel, A.: On the segmentation of 3d lidar point clouds. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2798–2805. IEEE (2011) – reference: Yang, H., Shi, J., Carlone, L.: TEASER: fast and certifiable point cloud registration. IEEE Trans. Robot. (2020) – reference: Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: Semantickitti: a dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9297–9307 (2019) – reference: FischlerMABollesRCRandom sample consensus: a paradigm for model fitting with applications to image analysis and automated cartographyCommun. ACM198124638139561815810.1145/358669.358692 – reference: JiménezVGodoyJArtuñedoAVillagraJGround segmentation algorithm for sloped terrain and sparse lidar point cloudIEEE Access2021913291413292710.1109/ACCESS.2021.3115664 – reference: ShenZLiangHLinLWangZHuangWYuJFast ground segmentation for 3d lidar point cloud based on jump-convolution-processRemote Sens.20211316323910.3390/rs13163239 – reference: Paigwar, A., Erkent, Ö., Sierra-Gonzalez, D., Laugier, C.: Gndnet: fast ground plane estimation and point cloud segmentation for autonomous vehicles. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2150–2156. IEEE (2020) – reference: Narksri, P., Takeuchi, E., Ninomiya, Y., Morales, Y., Akai, N., Kawaguchi, N.: A slope-robust cascaded ground segmentation in 3d point cloud for autonomous vehicles. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 497–504. IEEE (2018) – volume: 13 start-page: 3239 issue: 16 year: 2021 ident: 1593_CR7 publication-title: Remote Sens. doi: 10.3390/rs13163239 – ident: 1593_CR9 – ident: 1593_CR18 doi: 10.1109/IROS.2018.8594299 – ident: 1593_CR8 doi: 10.1109/IROS45743.2020.9340979 – ident: 1593_CR19 doi: 10.1109/ICRA48506.2021.9561364 – ident: 1593_CR24 doi: 10.1109/ITSC.2018.8569534 – ident: 1593_CR16 doi: 10.1109/IROS47612.2022.9981561 – ident: 1593_CR6 doi: 10.1109/ICCV48922.2021.01572 – ident: 1593_CR4 doi: 10.1109/ICCV.2019.00939 – volume: 23 start-page: 1357 issue: 2 year: 2022 ident: 1593_CR5 publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2022.3225293 – volume: 7 start-page: 7255 issue: 3 year: 2022 ident: 1593_CR17 publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2022.3182096 – ident: 1593_CR20 doi: 10.1109/UR55393.2022.9826238 – volume: 8 start-page: 1597 issue: 2 year: 2022 ident: 1593_CR15 publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2022.3187008 – volume: 24 start-page: 381 issue: 6 year: 1981 ident: 1593_CR12 publication-title: Commun. ACM doi: 10.1145/358669.358692 – ident: 1593_CR11 doi: 10.1109/ICRA.2017.7989591 – volume: 9 start-page: 132914 year: 2021 ident: 1593_CR14 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3115664 – volume: 6 start-page: 2272 issue: 2 year: 2021 ident: 1593_CR13 publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2021.3061363 – volume: 7 start-page: 12086 issue: 4 year: 2022 ident: 1593_CR21 publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2022.3201689 – ident: 1593_CR2 doi: 10.1109/ITSC.2015.133 – volume: 6 start-page: 6458 issue: 4 year: 2021 ident: 1593_CR3 publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2021.3093009 – ident: 1593_CR23 doi: 10.1109/TRO.2020.3033695 – ident: 1593_CR10 doi: 10.1109/IVS.2010.5548059 – volume: 43 start-page: 685 issue: 5 year: 2024 ident: 1593_CR22 publication-title: Int. J. Robot. Res. doi: 10.1177/02783649231207654 – ident: 1593_CR1 doi: 10.1109/ICRA.2011.5979818 |
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| Snippet | Ground segmentation is crucial for guiding mobile robots and identifying nearby objects. However, it should be noted that the ground often presents complex... |
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| SubjectTerms | Accuracy Algorithms Communications Engineering Computer Science Datasets Efficiency Image Processing and Computer Vision Image segmentation Methods Networks Pattern Recognition Semantics Task complexity |
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| Title | An efficient ground segmentation approach for LiDAR point cloud utilizing adjacent grids |
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