gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments
Ground segmentation plays an increasingly important role as a preprocessing module for numerous applications such as Simultaneous Localization and Mapping (SLAM), place recognition, and point cloud registration. The efficiency and accuracy of ground segmentation can directly influence the performanc...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10990224/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850134282074324992 |
|---|---|
| author | Truong Giang Dao Dinh Tuan Tran Duc Manh Nguyen Joo-Ho Lee Anh Quang Nguyen |
| author_facet | Truong Giang Dao Dinh Tuan Tran Duc Manh Nguyen Joo-Ho Lee Anh Quang Nguyen |
| author_sort | Truong Giang Dao |
| collection | DOAJ |
| description | Ground segmentation plays an increasingly important role as a preprocessing module for numerous applications such as Simultaneous Localization and Mapping (SLAM), place recognition, and point cloud registration. The efficiency and accuracy of ground segmentation can directly influence the performance of these applications. While several methods have been proposed, they continue to struggle to meet the comprehensive requirements of robust ground segmentation. To this end, we propose a ground segmentation method called gTRAVEL+, an improvement of TRAVEL that focuses exclusively on enhancing ground segmentation performance. Our method exploits a temporal and locally-aware noise removal strategy, termed Temporal Node-Wise Noise Removal (TNNR), which effectively eliminates noise points that compromise the ground plane fitting process. Additionally, Merge Node Plane Fitting (MNPF) is proposed to address issues of partial under-segmentation arising from suboptimal region sizing. Finally, Rejected Ground Node Revert (RNGR) ensures thorough geometric relationship verification, significantly improving true positives. Quantitative and qualitative experiments conducted on both urban and off-road environments show that our proposed method demonstrates superior performance, achieving a <inline-formula> <tex-math notation="LaTeX">${F} {_{{1}}}$ </tex-math></inline-formula>-score of 95.16% and competitive speed compared to state-of-the-art methods. |
| format | Article |
| id | doaj-art-ba6203633e83446ebe6d125b4fa334ff |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ba6203633e83446ebe6d125b4fa334ff2025-08-20T02:31:44ZengIEEEIEEE Access2169-35362025-01-0113824318244010.1109/ACCESS.2025.356762110990224gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road EnvironmentsTruong Giang Dao0https://orcid.org/0009-0005-3047-2441Dinh Tuan Tran1https://orcid.org/0000-0001-7443-9102Duc Manh Nguyen2https://orcid.org/0009-0006-0408-8179Joo-Ho Lee3https://orcid.org/0000-0003-1015-5615Anh Quang Nguyen4https://orcid.org/0000-0002-4198-4208School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamFaculty of Data Science, Shiga University, Hikone, JapanSchool of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamCollege of Information Science and Engineering, Ritsumeikan University, Kyoto, JapanSchool of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamGround segmentation plays an increasingly important role as a preprocessing module for numerous applications such as Simultaneous Localization and Mapping (SLAM), place recognition, and point cloud registration. The efficiency and accuracy of ground segmentation can directly influence the performance of these applications. While several methods have been proposed, they continue to struggle to meet the comprehensive requirements of robust ground segmentation. To this end, we propose a ground segmentation method called gTRAVEL+, an improvement of TRAVEL that focuses exclusively on enhancing ground segmentation performance. Our method exploits a temporal and locally-aware noise removal strategy, termed Temporal Node-Wise Noise Removal (TNNR), which effectively eliminates noise points that compromise the ground plane fitting process. Additionally, Merge Node Plane Fitting (MNPF) is proposed to address issues of partial under-segmentation arising from suboptimal region sizing. Finally, Rejected Ground Node Revert (RNGR) ensures thorough geometric relationship verification, significantly improving true positives. Quantitative and qualitative experiments conducted on both urban and off-road environments show that our proposed method demonstrates superior performance, achieving a <inline-formula> <tex-math notation="LaTeX">${F} {_{{1}}}$ </tex-math></inline-formula>-score of 95.16% and competitive speed compared to state-of-the-art methods.https://ieeexplore.ieee.org/document/10990224/Ground segmentationLiDAR-based perceptionmapping |
| spellingShingle | Truong Giang Dao Dinh Tuan Tran Duc Manh Nguyen Joo-Ho Lee Anh Quang Nguyen gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments IEEE Access Ground segmentation LiDAR-based perception mapping |
| title | gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments |
| title_full | gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments |
| title_fullStr | gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments |
| title_full_unstemmed | gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments |
| title_short | gTRAVEL+: Enhancing Ground Segmentation for 3D Point Cloud With Temporal Noise Removal and Adaptive Plane Fitting for Urban and Off-Road Environments |
| title_sort | gtravel enhancing ground segmentation for 3d point cloud with temporal noise removal and adaptive plane fitting for urban and off road environments |
| topic | Ground segmentation LiDAR-based perception mapping |
| url | https://ieeexplore.ieee.org/document/10990224/ |
| work_keys_str_mv | AT truonggiangdao gtravelenhancinggroundsegmentationfor3dpointcloudwithtemporalnoiseremovalandadaptiveplanefittingforurbanandoffroadenvironments AT dinhtuantran gtravelenhancinggroundsegmentationfor3dpointcloudwithtemporalnoiseremovalandadaptiveplanefittingforurbanandoffroadenvironments AT ducmanhnguyen gtravelenhancinggroundsegmentationfor3dpointcloudwithtemporalnoiseremovalandadaptiveplanefittingforurbanandoffroadenvironments AT jooholee gtravelenhancinggroundsegmentationfor3dpointcloudwithtemporalnoiseremovalandadaptiveplanefittingforurbanandoffroadenvironments AT anhquangnguyen gtravelenhancinggroundsegmentationfor3dpointcloudwithtemporalnoiseremovalandadaptiveplanefittingforurbanandoffroadenvironments |