Among Us: Adversarially Robust Collaborative Perception by Consensus
Among Us: Adversarially Robust Collaborative Perception by Consensus
Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. This could be addressed by the adversarial defense, but its training requires the often-unknown attacking mechanism. Differently, we propose ROBOSAC, a novel sampling-based defense strategy generalizable to …