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| Main Authors: | , , , , |
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| Format: | Preprint |
| Izdano: |
2025
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| Teme: | |
| Online dostop: | https://arxiv.org/abs/2502.13708 |
| Oznake: |
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| _version_ | 1866914026821255168 |
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| author | Crocetti, Francesco Dionigi, Alberto Brilli, Raffaele Costante, Gabriele Valigi, Paolo |
| author_facet | Crocetti, Francesco Dionigi, Alberto Brilli, Raffaele Costante, Gabriele Valigi, Paolo |
| contents | Visual Odometry (VO) and Visual SLAM (V-SLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75% with respect to a traditional fixed lighting technique. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13708 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Active Illumination for Visual Ego-Motion Estimation in the Dark Crocetti, Francesco Dionigi, Alberto Brilli, Raffaele Costante, Gabriele Valigi, Paolo Robotics Visual Odometry (VO) and Visual SLAM (V-SLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75% with respect to a traditional fixed lighting technique. |
| title | Active Illumination for Visual Ego-Motion Estimation in the Dark |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.13708 |