Multi-LED Classification as Pretext For Robot Heading Estimation
<|reference_start|>Multi-LED Classification as Pretext For Robot Heading Estimation: 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.<|reference_end|>
@article{carlotti2024multi-led,
    title={Multi-LED Classification as Pretext For Robot Heading Estimation},
    author={Nicholas Carlotti, Mirko Nava, Alessandro Giusti},
    journal={arXiv preprint arXiv:2410.04536},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04536},
    primaryClass={cs.RO}
}