Multi-LED Classification as Pretext For Robot Heading Estimation
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913535381995520 |
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| author | Carlotti, Nicholas Nava, Mirko Giusti, Alessandro |
| author_facet | Carlotti, Nicholas Nava, Mirko Giusti, Alessandro |
| contents | We propose a self-supervised approach for visual robot detection and heading estimation by learning to estimate the states (OFF or ON) of four independent robot-mounted LEDs. Experimental results show a median image-space position error of 14 px and relative heading MAE of 17 degrees, versus a supervised upperbound scoring 10 px and 8 degrees, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04536 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-LED Classification as Pretext For Robot Heading Estimation Carlotti, Nicholas Nava, Mirko Giusti, Alessandro Robotics We propose a self-supervised approach for visual robot detection and heading estimation by learning to estimate the states (OFF or ON) of four independent robot-mounted LEDs. Experimental results show a median image-space position error of 14 px and relative heading MAE of 17 degrees, versus a supervised upperbound scoring 10 px and 8 degrees, respectively. |
| title | Multi-LED Classification as Pretext For Robot Heading Estimation |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.04536 |