Correspondence identification (CoID) is an essential component for collaborative perception in multi-robot systems, such as connected autonomous vehicles. The goal of CoID is to identify the correspondence of objects observed by multiple robots in their own field of view in order for robots to consistently refer to the same objects. CoID is challenging due to perceptual aliasing, object non-covisibility, and noisy sensing. In this paper, we introduce a novel deep masked graph matching approach to enable CoID and address the challenges. Our approach formulates CoID as a graph matching problem and we design a masked neural network to integrate the multimodal visual, spatial, and GPS information to perform CoID. In addition, we design a new technique to explicitly address object non-covisibility caused by occlusion and the vehicle's limited field of view. We evaluate our approach in a variety of street environments using a high-fidelity simulation that integrates the CARLA and SUMO simulators. The experimental results show that our approach outperforms the previous approaches and achieves state-of-the-art CoID performance in connected autonomous driving applications. Our work is available at: https://github.com/gaopeng5/DMGM.git.

翻译：对应识别（CoID）是多机器人系统（如网联自动驾驶车辆）协同感知的关键组成部分。CoID的目标是识别多个机器人在各自视野中观测到的物体之间的对应关系，从而使机器人能够一致地指代同一物体。由于感知混淆、物体不可共视以及噪声感知等因素，CoID具有挑战性。本文提出一种新颖的深度掩码图匹配方法，以支持CoID并应对上述挑战。该方法将CoID形式化为图匹配问题，并设计了一种掩码神经网络，整合多模态视觉、空间和GPS信息以执行CoID。此外，我们还设计了一种新技术，显式处理由遮挡和车辆有限视野导致的物体不可共视问题。我们利用集成CARLA和SUMO模拟器的高保真仿真环境，在多种街道场景下评估了该方法。实验结果表明，所提方法优于先前方法，并在网联自动驾驶应用中达到了最先进的CoID性能。相关代码已开源：https://github.com/gaopeng5/DMGM.git