Paper ID: 2410.04536

Multi-LED Classification as Pretext For Robot Heading Estimation

Nicholas Carlotti, Mirko Nava, Alessandro Giusti

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.

Submitted: Oct 6, 2024