Object Covisibility Graph for Change Detection and 3D Object-Oriented Map Revision in Semi-Static Scenes

Recently, Object-oriented SLAM(Object SLAM) has attracted extensive research due to its ability to perceive the environment at a 3d object level. Existing object SLAM methods mostly focus on constructing 3d object map for static objects or mitigating the impact of currently dynamic objects on localization and mapping. However, detection of semi-static objects whose position change while unobserved still pose a significant challenge, resulting in outdated maps, which could lead to localization and robot application failures. In this paper, we propose a method to compare current observation with the existing map, enabling the continuous detection and updating of outdated sections within the map caused by position-changing semi-static objects. First, we introduce Object Covisibility Graph(OCG) to maintain the historically observed co-visibility relationships between objects. Building on this, we design an algorithm that uses the OCG to determine whether the current camera is within the observable region of each object, and subsequently implement an object state updating algorithm to detect and update outdated sections continuously. We conduct experiments on our self-make dataset with changing objects and a dataset with only static objects. The experimental results show that our method updates the outdated parts of the map more effectively compared to previous studies.