Multi Agent Collaborative Perception

Multi-agent collaborative perception focuses on enabling multiple agents (e.g., robots, autonomous vehicles) to collectively perceive their environment more comprehensively than any single agent could alone, improving robustness and efficiency. Current research emphasizes developing efficient communication strategies and fusion algorithms, often employing neural networks like transformers and graph attention networks, or state-space models, to integrate diverse sensor data while minimizing bandwidth consumption. This field is crucial for advancing autonomous systems, particularly in robotics and autonomous driving, by enabling more reliable and robust perception in complex and dynamic environments.