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

翻译：协同感知因其能够解决单智能体感知中的若干固有挑战（如遮挡与超视距问题）而备受关注。然而，现有的协同感知系统严重依赖精确定位系统来建立智能体间统一的空间坐标系。这种依赖性使其易受较大位姿误差或恶意攻击的影响，导致感知性能显著下降。为解决此问题，我们提出了一种新型自定位协同感知系统~$\mathtt{CoBEVGlue}$，该系统无需借助外部定位系统即可实现更全面、更鲁棒的协同。$\mathtt{CoBEVGlue}$的核心是一个新颖的空间对齐模块，该模块通过高效匹配跨智能体的共视目标来提供智能体间的相对位姿。我们在真实世界与仿真数据集上验证了所提方法。结果表明：i) $\mathtt{CoBEVGlue}$在任意定位噪声与攻击下均实现了最先进的检测性能；ii) 该空间对齐模块能够与大多数现有方法无缝集成，平均提升其性能达$57.7\%$。代码发布于 https://github.com/VincentNi0107/CoBEVGlue