Autonomous systems require robots, agents, that can understand and interpret their surroundings. This will allow them to enhance their decision-making processes and potentially minimize risk in downstream tasks. With developments in an individual agent’s ability to perceive its surroundings, we can extend to using multiple agents that work together in a system. This multi-agent system (MAS) enables multiple robots to collaborate while exploring the same environment efficiently for full scene understanding and analysis. The deployment of a MAS also allows for the capture of additional information and the potential for exploration of larger environments. Collaborative MASs require correspondence identification that enables each agent to refer to the same objects (i.e., hazards, science targets, etc.) within their own field of view. Yet, this process of correspondence identification has challenges that arise when agents experience non-covisibility of objects, i.e., objects that are not visible in all fields of view. Our approach deploys an object detection model to capture RGB+D images on a stereovision camera and to develop a graph-matching algorithm to identify the similarities between objects in different fields of view. This graph-matching algorithm will better handle the challenges of correspondence identification, non-covisibility, with higher certainty than previously attempted assignment algorithms. Additionally, compared to the assignment algorithm, the graph-matching approach will produce less computationally expensive results, which can be critical for resource-limited agents or tasks. This enhanced correspondence identification develops a world reference frame so that the agents within the system can perform their downstream tasks strategically and efficiently.