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

提出了一种新颖的自定位协同感知系统CoBEVGlue，它通过有效匹配跨代理间的共可见对象，实现更全面、更稳健的协同感知，无需使用外部定位系统。CoBEVGlue的核心是一个新颖的空间对齐模块，它提供了代理间的相对位姿，从而验证了我们的方法在真实和模拟数据集上的有效性。结果表明，CoBEVGlue在任意定位噪声和攻击下取得了最先进的检测性能，并且空间对齐模块能够无缝集成大部分先前的方法，并将其性能提升了平均57.7%。

自定位的协作感知