Self-Localized Collaborative Perception

• URL: http://arxiv.org/abs/2406.12712v1
• Date: Tue, 18 Jun 2024 15:26:54 GMT
• Title: Self-Localized Collaborative Perception
• Authors: Zhenyang Ni, Zixing Lei, Yifan Lu, Dingju Wang, Chen Feng, Yanfeng Wang, Siheng Chen,
• Abstract summary: We propose$mathttCoBEVGlue$, a novel self-localized collaborative perception system. $mathttCoBEVGlue$ is a novel spatial alignment module, which provides the relative poses between agents. $mathttCoBEVGlue$ achieves state-of-the-art detection performance under arbitrary localization noises and attacks.
• Score: 49.86110931859302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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