Self-Localized Collaborative Perception
<|reference_start|>Self-Localized Collaborative Perception: 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<|reference_end|>
@article{ni2024self-localized,
    title={Self-Localized Collaborative Perception},
    author={Zhenyang Ni, Zixing Lei, Yifan Lu, Dingju Wang, Chen Feng, Yanfeng
  Wang, Siheng Chen},
    journal={arXiv preprint arXiv:2406.12712},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12712},
    primaryClass={cs.CV}
}