Motion Constrained Point Cloud Matching for Maritime Tracking
Extended Object Tracking (EOT) plays a critical role in accurately estimating the state of nearby vessels in confined regions, such as urban waterways. By estimating a vessel’s extent, it is possible to improve velocity estimation and motion prediction. Traditional EOT methods in the mari...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11048472/ |
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| author | Nicholas Dalhaug Martin Baerveldt Angelica I. Aviles-Rivero Carola-Bibiane Schonlieb Annette Stahl Rudolf Mester Edmund Forland Brekke |
| author_facet | Nicholas Dalhaug Martin Baerveldt Angelica I. Aviles-Rivero Carola-Bibiane Schonlieb Annette Stahl Rudolf Mester Edmund Forland Brekke |
| author_sort | Nicholas Dalhaug |
| collection | DOAJ |
| description | Extended Object Tracking (EOT) plays a critical role in accurately estimating the state of nearby vessels in confined regions, such as urban waterways. By estimating a vessel’s extent, it is possible to improve velocity estimation and motion prediction. Traditional EOT methods in the maritime domain that use point clouds from Light Detection And Ranging (LiDAR) commonly parameterize target extents. However, these approaches often struggle with data association and clutter removal, particularly when using 2D parameterizations. To address these limitations, we propose a 3D EOT framework leveraging motion-constrained Iterative Closest Point (ICP) displacements integrated into an Error State Kalman Filter (ESKF). Our approach incorporates maritime-specific motion constraints, enabling robust target tracking even with sparse measurements. Evaluation on real-world maritime data demonstrates that the proposed 3D tracker significantly outperforms a 2D Gaussian Process Extended Object Tracking (GP-EOT) tracker, offering enhanced resilience against wake-induced noise and delivering superior state and extent estimation accuracy. |
| format | Article |
| id | doaj-art-78eee73fb01e40358cd1bbff411b5fbb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-78eee73fb01e40358cd1bbff411b5fbb2025-08-20T03:28:48ZengIEEEIEEE Access2169-35362025-01-011311135411137110.1109/ACCESS.2025.358232711048472Motion Constrained Point Cloud Matching for Maritime TrackingNicholas Dalhaug0https://orcid.org/0009-0007-7824-5904Martin Baerveldt1https://orcid.org/0009-0008-6485-487XAngelica I. Aviles-Rivero2https://orcid.org/0000-0002-8878-0325Carola-Bibiane Schonlieb3https://orcid.org/0000-0003-0099-6306Annette Stahl4Rudolf Mester5Edmund Forland Brekke6https://orcid.org/0000-0001-8735-1687Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayDepartment of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayYau Mathematical Sciences Center, Tsinghua University, Beijing, ChinaDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K.Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayDepartment of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayDepartment of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayExtended Object Tracking (EOT) plays a critical role in accurately estimating the state of nearby vessels in confined regions, such as urban waterways. By estimating a vessel’s extent, it is possible to improve velocity estimation and motion prediction. Traditional EOT methods in the maritime domain that use point clouds from Light Detection And Ranging (LiDAR) commonly parameterize target extents. However, these approaches often struggle with data association and clutter removal, particularly when using 2D parameterizations. To address these limitations, we propose a 3D EOT framework leveraging motion-constrained Iterative Closest Point (ICP) displacements integrated into an Error State Kalman Filter (ESKF). Our approach incorporates maritime-specific motion constraints, enabling robust target tracking even with sparse measurements. Evaluation on real-world maritime data demonstrates that the proposed 3D tracker significantly outperforms a 2D Gaussian Process Extended Object Tracking (GP-EOT) tracker, offering enhanced resilience against wake-induced noise and delivering superior state and extent estimation accuracy.https://ieeexplore.ieee.org/document/11048472/EOTICPLiDARmaritimetarget tracking |
| spellingShingle | Nicholas Dalhaug Martin Baerveldt Angelica I. Aviles-Rivero Carola-Bibiane Schonlieb Annette Stahl Rudolf Mester Edmund Forland Brekke Motion Constrained Point Cloud Matching for Maritime Tracking IEEE Access EOT ICP LiDAR maritime target tracking |
| title | Motion Constrained Point Cloud Matching for Maritime Tracking |
| title_full | Motion Constrained Point Cloud Matching for Maritime Tracking |
| title_fullStr | Motion Constrained Point Cloud Matching for Maritime Tracking |
| title_full_unstemmed | Motion Constrained Point Cloud Matching for Maritime Tracking |
| title_short | Motion Constrained Point Cloud Matching for Maritime Tracking |
| title_sort | motion constrained point cloud matching for maritime tracking |
| topic | EOT ICP LiDAR maritime target tracking |
| url | https://ieeexplore.ieee.org/document/11048472/ |
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