PanopticNDT: Efficient and Robust Panoptic Mapping
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866913408485425152 |
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| author | Seichter, Daniel Stephan, Benedict Fischedick, Söhnke Benedikt Müller, Steffen Rabes, Leonard Gross, Horst-Michael |
| author_facet | Seichter, Daniel Stephan, Benedict Fischedick, Söhnke Benedikt Müller, Steffen Rabes, Leonard Gross, Horst-Michael |
| contents | As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent is, and how they can be reached; i.e., information about free space is also crucial. Panoptic mapping is a powerful instrument providing such information. However, building 3D panoptic maps with high spatial resolution is challenging on mobile robots, given their limited computing capabilities. In this paper, we propose PanopticNDT - an efficient and robust panoptic mapping approach based on occupancy normal distribution transform (NDT) mapping. We evaluate our approach on the publicly available datasets Hypersim and ScanNetV2. The results reveal that our approach can represent panoptic information at a higher level of detail than other state-of-the-art approaches while enabling real-time panoptic mapping on mobile robots. Finally, we prove the real-world applicability of PanopticNDT with qualitative results in a domestic application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13635 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PanopticNDT: Efficient and Robust Panoptic Mapping Seichter, Daniel Stephan, Benedict Fischedick, Söhnke Benedikt Müller, Steffen Rabes, Leonard Gross, Horst-Michael Robotics Artificial Intelligence Machine Learning As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent is, and how they can be reached; i.e., information about free space is also crucial. Panoptic mapping is a powerful instrument providing such information. However, building 3D panoptic maps with high spatial resolution is challenging on mobile robots, given their limited computing capabilities. In this paper, we propose PanopticNDT - an efficient and robust panoptic mapping approach based on occupancy normal distribution transform (NDT) mapping. We evaluate our approach on the publicly available datasets Hypersim and ScanNetV2. The results reveal that our approach can represent panoptic information at a higher level of detail than other state-of-the-art approaches while enabling real-time panoptic mapping on mobile robots. Finally, we prove the real-world applicability of PanopticNDT with qualitative results in a domestic application. |
| title | PanopticNDT: Efficient and Robust Panoptic Mapping |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2309.13635 |