Optimizing plane detection in point clouds through line sampling
Abstract Plane detection in point clouds is a common step in interpreting environments within robotics. Mobile robotic platforms must interact efficiently and safely with their surroundings, which requires capabilities such as detecting walls to avoid collisions and recognizing workbenches for objec...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12660-w |
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| _version_ | 1849226474156982272 |
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| author | José María Martínez-Otzeta Jon Azpiazu Iñigo Mendialdua Basilio Sierra |
| author_facet | José María Martínez-Otzeta Jon Azpiazu Iñigo Mendialdua Basilio Sierra |
| author_sort | José María Martínez-Otzeta |
| collection | DOAJ |
| description | Abstract Plane detection in point clouds is a common step in interpreting environments within robotics. Mobile robotic platforms must interact efficiently and safely with their surroundings, which requires capabilities such as detecting walls to avoid collisions and recognizing workbenches for object manipulation. Since these environmental elements typically appear as plane-shaped surfaces, a fast and accurate plane detector is an essential tool for robotics practitioners. RANSAC (Random Sample Consensus) is a widely used technique for plane detection that iteratively evaluates the fitness of planes by sampling three points at a time from a point cloud. In this work, we present an approach that, rather than seeking planes directly, focuses on finding lines by sampling only two points at a time. This leverages the observation that it is more likely to detect lines within the plane than to find the plane itself. To estimate planes from these lines, we perform an additional step that fits a plane for each pair of lines. Experiments conducted on three datasets, two of which are public, demonstrate that our approach outperforms the traditional RANSAC method, achieving better results while requiring fewer iterations. A public repository containing the developed code is also provided. |
| format | Article |
| id | doaj-art-0caf106d7f9c4c50ae7bebea598b2b6e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0caf106d7f9c4c50ae7bebea598b2b6e2025-08-24T11:17:20ZengNature PortfolioScientific Reports2045-23222025-08-0115111410.1038/s41598-025-12660-wOptimizing plane detection in point clouds through line samplingJosé María Martínez-Otzeta0Jon Azpiazu1Iñigo Mendialdua2Basilio Sierra3Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU)Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU)Department of Languages and Information Systems, University of the Basque Country (UPV/EHU)Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU)Abstract Plane detection in point clouds is a common step in interpreting environments within robotics. Mobile robotic platforms must interact efficiently and safely with their surroundings, which requires capabilities such as detecting walls to avoid collisions and recognizing workbenches for object manipulation. Since these environmental elements typically appear as plane-shaped surfaces, a fast and accurate plane detector is an essential tool for robotics practitioners. RANSAC (Random Sample Consensus) is a widely used technique for plane detection that iteratively evaluates the fitness of planes by sampling three points at a time from a point cloud. In this work, we present an approach that, rather than seeking planes directly, focuses on finding lines by sampling only two points at a time. This leverages the observation that it is more likely to detect lines within the plane than to find the plane itself. To estimate planes from these lines, we perform an additional step that fits a plane for each pair of lines. Experiments conducted on three datasets, two of which are public, demonstrate that our approach outperforms the traditional RANSAC method, achieving better results while requiring fewer iterations. A public repository containing the developed code is also provided.https://doi.org/10.1038/s41598-025-12660-wRandom sample consensusPlane detectionPoint cloud segmentationRobotics |
| spellingShingle | José María Martínez-Otzeta Jon Azpiazu Iñigo Mendialdua Basilio Sierra Optimizing plane detection in point clouds through line sampling Scientific Reports Random sample consensus Plane detection Point cloud segmentation Robotics |
| title | Optimizing plane detection in point clouds through line sampling |
| title_full | Optimizing plane detection in point clouds through line sampling |
| title_fullStr | Optimizing plane detection in point clouds through line sampling |
| title_full_unstemmed | Optimizing plane detection in point clouds through line sampling |
| title_short | Optimizing plane detection in point clouds through line sampling |
| title_sort | optimizing plane detection in point clouds through line sampling |
| topic | Random sample consensus Plane detection Point cloud segmentation Robotics |
| url | https://doi.org/10.1038/s41598-025-12660-w |
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