Self-Localized Collaborative Perception
This addresses robustness issues in multi-agent perception for applications like autonomous vehicles, though it is incremental as it builds on existing collaborative perception frameworks.
The paper tackles the reliance of collaborative perception systems on precise external localization, which makes them vulnerable to errors and attacks, by proposing CoBEVGlue, a self-localized system that achieves state-of-the-art detection performance under arbitrary localization noises and attacks and enhances previous methods by an average of 57.7%.
Collaborative perception has garnered considerable attention due to its capacity to address several inherent challenges in single-agent perception, including occlusion and out-of-range issues. However, existing collaborative perception systems heavily rely on precise localization systems to establish a consistent spatial coordinate system between agents. This reliance makes them susceptible to large pose errors or malicious attacks, resulting in substantial reductions in perception performance. To address this, we propose~$\mathtt{CoBEVGlue}$, a novel self-localized collaborative perception system, which achieves more holistic and robust collaboration without using an external localization system. The core of~$\mathtt{CoBEVGlue}$ is a novel spatial alignment module, which provides the relative poses between agents by effectively matching co-visible objects across agents. We validate our method on both real-world and simulated datasets. The results show that i) $\mathtt{CoBEVGlue}$ achieves state-of-the-art detection performance under arbitrary localization noises and attacks; and ii) the spatial alignment module can seamlessly integrate with a majority of previous methods, enhancing their performance by an average of $57.7\%$. Code is available at https://github.com/VincentNi0107/CoBEVGlue