Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915753801809920 |
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| author | Xie, Zhanteng Pan, Yipeng Zhang, Yinqiang Pan, Jia Dames, Philip |
| author_facet | Xie, Zhanteng Pan, Yipeng Zhang, Yinqiang Pan, Jia Dames, Philip |
| contents | This article presents a complete semantic scene understanding workflow using only a single 2D lidar. This fills the gap in 2D lidar semantic segmentation, thereby enabling the rethinking and enhancement of existing 2D lidar-based algorithms for application in various mobile robot tasks. It introduces the first publicly available 2D lidar semantic segmentation dataset and the first fine-grained semantic segmentation algorithm specifically designed for 2D lidar sensors on autonomous mobile robots. To annotate this dataset, we propose a novel semi-automatic semantic labeling framework that requires minimal human effort and provides point-level semantic annotations. The data was collected by three different types of 2D lidar sensors across twelve indoor environments, featuring a range of common indoor objects. Furthermore, the proposed semantic segmentation algorithm fully exploits raw lidar information -- position, range, intensity, and incident angle -- to deliver stochastic, point-wise semantic segmentation. We present a series of semantic occupancy grid mapping experiments and demonstrate two semantically-aware navigation control policies based on 2D lidar. These results demonstrate that the proposed semantic 2D lidar dataset, semi-automatic labeling framework, and segmentation algorithm are effective and can enhance different components of the robotic navigation pipeline. Multimedia resources are available at: https://youtu.be/P1Hsvj6WUSY. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_09899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone Xie, Zhanteng Pan, Yipeng Zhang, Yinqiang Pan, Jia Dames, Philip Robotics This article presents a complete semantic scene understanding workflow using only a single 2D lidar. This fills the gap in 2D lidar semantic segmentation, thereby enabling the rethinking and enhancement of existing 2D lidar-based algorithms for application in various mobile robot tasks. It introduces the first publicly available 2D lidar semantic segmentation dataset and the first fine-grained semantic segmentation algorithm specifically designed for 2D lidar sensors on autonomous mobile robots. To annotate this dataset, we propose a novel semi-automatic semantic labeling framework that requires minimal human effort and provides point-level semantic annotations. The data was collected by three different types of 2D lidar sensors across twelve indoor environments, featuring a range of common indoor objects. Furthermore, the proposed semantic segmentation algorithm fully exploits raw lidar information -- position, range, intensity, and incident angle -- to deliver stochastic, point-wise semantic segmentation. We present a series of semantic occupancy grid mapping experiments and demonstrate two semantically-aware navigation control policies based on 2D lidar. These results demonstrate that the proposed semantic 2D lidar dataset, semi-automatic labeling framework, and segmentation algorithm are effective and can enhance different components of the robotic navigation pipeline. Multimedia resources are available at: https://youtu.be/P1Hsvj6WUSY. |
| title | Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.09899 |