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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.04979 |
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| _version_ | 1866917744503422976 |
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| author | Niecksch, Lennart Mock, Alexander Igelbrink, Felix Wiemann, Thomas Hertzberg, Joachim |
| author_facet | Niecksch, Lennart Mock, Alexander Igelbrink, Felix Wiemann, Thomas Hertzberg, Joachim |
| contents | In this paper, we present a novel method for 3D geometric scene graph generation using range sensors and RGB cameras. We initially detect instance-wise keypoints with a YOLOv8s model to compute 6D pose estimates of known objects by solving PnP. We use a ray tracing approach to track a geometric scene graph consisting of mesh models of object instances. In contrast to classical point-to-point matching, this leads to more robust results, especially under occlusions between objects instances. We show that using this hybrid strategy leads to robust self-localization, pre-segmentation of the range sensor data and accurate pose tracking of objects using the same environmental representation. All detected objects are integrated into a semantic scene graph. This scene graph then serves as a front end to a semantic mapping framework to allow spatial reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04979 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mesh-based Object Tracking for Dynamic Semantic 3D Scene Graphs via Ray Tracing Niecksch, Lennart Mock, Alexander Igelbrink, Felix Wiemann, Thomas Hertzberg, Joachim Robotics In this paper, we present a novel method for 3D geometric scene graph generation using range sensors and RGB cameras. We initially detect instance-wise keypoints with a YOLOv8s model to compute 6D pose estimates of known objects by solving PnP. We use a ray tracing approach to track a geometric scene graph consisting of mesh models of object instances. In contrast to classical point-to-point matching, this leads to more robust results, especially under occlusions between objects instances. We show that using this hybrid strategy leads to robust self-localization, pre-segmentation of the range sensor data and accurate pose tracking of objects using the same environmental representation. All detected objects are integrated into a semantic scene graph. This scene graph then serves as a front end to a semantic mapping framework to allow spatial reasoning. |
| title | Mesh-based Object Tracking for Dynamic Semantic 3D Scene Graphs via Ray Tracing |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.04979 |