Cooperative perception is a promising technique for enhancing the perception capabilities of automated vehicles through vehicle-to-everything (V2X) cooperation, provided that accurate relative pose transforms are available. Nevertheless, obtaining precise positioning information often entails high costs associated with navigation systems. Moreover, signal drift resulting from factors such as occlusion and multipath effects can compromise the stability of the positioning information. Hence, a low-cost and robust method is required to calibrate relative pose information for multi-agent cooperative perception. In this paper, we propose a simple but effective inter-agent object association approach (CBM), which constructs contexts using the detected bounding boxes, followed by local context matching and global consensus maximization. Based on the matched correspondences, optimal relative pose transform is estimated, followed by cooperative perception fusion. Extensive experimental studies are conducted on both the simulated and real-world datasets, high object association precision and decimeter level relative pose calibration accuracy is achieved among the cooperating agents even with larger inter-agent localization errors. Furthermore, the proposed approach outperforms the state-of-the-art methods in terms of object association and relative pose estimation accuracy, as well as the robustness of cooperative perception against the pose errors of the connected agents. The code will be available at https://github.com/zhyingS/CBM.

翻译：协同感知是一种通过车辆与万物（V2X）协作来增强自动驾驶车辆感知能力的有前景技术，前提是具备精确的相对位姿变换。然而，获取精确定位信息通常需要高昂的导航系统成本。此外，由遮挡和多径效应等因素引起的信号漂移会损害定位信息的稳定性。因此，需要一种低成本且鲁棒的方法来校准多智能体协同感知中的相对位姿信息。本文提出了一种简单而有效的智能体间目标关联方法（CBM），该方法利用检测到的边界框构建上下文，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，并在此基础上进行协同感知融合。在模拟和真实数据集上进行了大量实验研究，即使在智能体间定位误差较大的情况下，所提方法仍能在协作智能体间实现高目标关联精度和分米级相对位姿校准精度。此外，所提方法在目标关联和相对位姿估计精度方面，以及针对连接智能体位姿误差的协同感知鲁棒性上，均优于现有最优方法。代码将公开于https://github.com/zhyingS/CBM。