Cooperative perception is a promising technique for intelligent and connected vehicles through vehicle-to-everything (V2X) cooperation, provided that accurate pose information and relative pose transforms are available. Nevertheless, obtaining precise positioning information often entails high costs associated with navigation systems. {Hence, it is required to calibrate relative pose information for multi-agent cooperative perception.} This paper proposes a simple but effective object association approach named context-based matching (CBM), which identifies inter-agent object correspondences using intra-agent geometrical context. In detail, this method constructs contexts using the relative position of the detected bounding boxes, followed by local context matching and global consensus maximization. The optimal relative pose transform is estimated based on the matched correspondences, followed by cooperative perception fusion. Extensive experiments are conducted on both the simulated and real-world datasets. Even with larger inter-agent localization errors, high object association precision and decimeter-level relative pose calibration accuracy are achieved among the cooperating agents.

翻译：协同感知通过车联网（V2X）协作，为智能网联车辆提供了一种有前景的技术方案，前提是具备准确的位姿信息与相对位姿变换。然而，获取精准定位信息通常需要昂贵的导航系统成本。因此，必须对多智能体协同感知中的相对位姿信息进行标定。本文提出一种简单而有效的目标关联方法——基于上下文的匹配（CBM），该方法利用智能体内部的几何上下文识别智能体间的目标对应关系。具体而言，该方法通过检测边界框的相对位置构建上下文，随后进行局部上下文匹配与全局共识最大化。基于匹配的对应关系估计最优相对位姿变换，进而实现协同感知融合。在仿真与真实数据集上开展了大量实验。即使存在较大的智能体间定位误差，协作智能体仍能实现高精度的目标关联与分米级的相对位姿标定精度。