CoRA: A Collaborative Robust Architecture with Hybrid Fusion for Efficient Perception
Gong Chen, Chaokun Zhang, Pengcheng Lv, Xiaohui Xie

TL;DR
CoRA is a novel architecture for collaborative perception that combines feature-level fusion and object-level correction to enhance robustness and efficiency under adverse communication conditions.
Contribution
It introduces a hybrid fusion approach that decouples performance from robustness, improving efficiency and resilience in collaborative perception systems.
Findings
Improves [email protected] by ~19% under extreme scenarios
Reduces communication volume by over 5x
Demonstrates robustness against pose errors
Abstract
Collaborative perception has garnered significant attention as a crucial technology to overcome the perceptual limitations of single-agent systems. Many state-of-the-art (SOTA) methods have achieved communication efficiency and high performance via intermediate fusion. However, they share a critical vulnerability: their performance degrades under adverse communication conditions due to the misalignment induced by data transmission, which severely hampers their practical deployment. To bridge this gap, we re-examine different fusion paradigms, and recover that the strengths of intermediate and late fusion are not a trade-off, but a complementary pairing. Based on this key insight, we propose CoRA, a novel collaborative robust architecture with a hybrid approach to decouple performance from robustness with low communication. It is composed of two components: a feature-level fusion branch…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Distributed Sensor Networks and Detection Algorithms · Underwater Vehicles and Communication Systems
