Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908529355390976 |
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| author | Reichert, Hannes Serfling, Benjamin Schüssler, Elijah Turacan, Kerim Doll, Konrad Sick, Bernhard |
| author_facet | Reichert, Hannes Serfling, Benjamin Schüssler, Elijah Turacan, Kerim Doll, Konrad Sick, Bernhard |
| contents | In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems and autonomous vehicles. However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints. This study introduces a novel semantic segmentation framework tailored for modern high-resolution LiDAR sensors that addresses both accuracy and real-time processing demands. We propose a novel LiDAR dataset collected by a cutting-edge automotive 128 layer LiDAR in urban traffic scenes. Furthermore, we propose a semantic segmentation method utilizing surface normals as strong input features. Our approach is bridging the gap between cutting-edge research and practical automotive applications. Additionaly, we provide a Robot Operating System (ROS2) implementation that we operate on our research vehicle. Our dataset and code are publicly available: https://github.com/kav-institute/SemanticLiDAR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Reichert, Hannes Serfling, Benjamin Schüssler, Elijah Turacan, Kerim Doll, Konrad Sick, Bernhard Robotics Machine Learning In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems and autonomous vehicles. However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints. This study introduces a novel semantic segmentation framework tailored for modern high-resolution LiDAR sensors that addresses both accuracy and real-time processing demands. We propose a novel LiDAR dataset collected by a cutting-edge automotive 128 layer LiDAR in urban traffic scenes. Furthermore, we propose a semantic segmentation method utilizing surface normals as strong input features. Our approach is bridging the gap between cutting-edge research and practical automotive applications. Additionaly, we provide a Robot Operating System (ROS2) implementation that we operate on our research vehicle. Our dataset and code are publicly available: https://github.com/kav-institute/SemanticLiDAR. |
| title | Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2504.21602 |