Adaptive Sampling-based Particle Filter for Visual-inertial Gimbal in the Wild
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866910288652009472 |
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| author | Kang, Xueyang Herrera, Ariel Lema, Henry Valencia, Esteban Vandewalle, Patrick |
| author_facet | Kang, Xueyang Herrera, Ariel Lema, Henry Valencia, Esteban Vandewalle, Patrick |
| contents | In this paper, we present a Computer Vision (CV) based tracking and fusion algorithm, dedicated to a 3D printed gimbal system on drones operating in nature. The whole gimbal system can stabilize the camera orientation robustly in a challenging nature scenario by using skyline and ground plane as references. Our main contributions are the following: a) a light-weight Resnet-18 backbone network model was trained from scratch, and deployed onto the Jetson Nano platform to segment the image into binary parts (ground and sky); b) our geometry assumption from nature cues delivers the potential for robust visual tracking by using the skyline and ground plane as a reference; c) a spherical surface-based adaptive particle sampling, can fuse orientation from multiple sensor sources flexibly. The whole algorithm pipeline is tested on our customized gimbal module including Jetson and other hardware components. The experiments were performed on top of a building in the real landscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_10981 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Adaptive Sampling-based Particle Filter for Visual-inertial Gimbal in the Wild Kang, Xueyang Herrera, Ariel Lema, Henry Valencia, Esteban Vandewalle, Patrick Robotics Systems and Control Image and Video Processing 68T45, 57-06, 60B05 I.4.6; I.2.10 In this paper, we present a Computer Vision (CV) based tracking and fusion algorithm, dedicated to a 3D printed gimbal system on drones operating in nature. The whole gimbal system can stabilize the camera orientation robustly in a challenging nature scenario by using skyline and ground plane as references. Our main contributions are the following: a) a light-weight Resnet-18 backbone network model was trained from scratch, and deployed onto the Jetson Nano platform to segment the image into binary parts (ground and sky); b) our geometry assumption from nature cues delivers the potential for robust visual tracking by using the skyline and ground plane as a reference; c) a spherical surface-based adaptive particle sampling, can fuse orientation from multiple sensor sources flexibly. The whole algorithm pipeline is tested on our customized gimbal module including Jetson and other hardware components. The experiments were performed on top of a building in the real landscape. |
| title | Adaptive Sampling-based Particle Filter for Visual-inertial Gimbal in the Wild |
| topic | Robotics Systems and Control Image and Video Processing 68T45, 57-06, 60B05 I.4.6; I.2.10 |
| url | https://arxiv.org/abs/2206.10981 |