<p>Efficient management of indoor spaces is increasingly critical for applications such as security, evacuation planning, and robotic deployment. Digital twin technology has emerged as a transformative solution, providing a real-time link between the physical environment and its virtual counterpart to enable monitoring, simulation, analysis, and performance optimization. This paper introduces a novel collaborative approach to semantic mapping that enhances digital twins with semantic maps enriched with contextual information about the environment. Designed specifically for controlled indoor settings, the approach assumes the availability of prior knowledge through 3D CAD models and a managed environment. The proposed method leverages a state-of-the-art single-robot semantic mapping technique to collect semantic information using an RGB-D camera, integrating object detection, scene segmentation, and computational geometry to generate detailed point clouds and define object occupancy zones. Building on this foundation, a collaborative framework is developed for maintaining and updating the semantic map within the digital twin. Autonomous mobile robots generate individual semantic maps, which are communicated to the digital twin and incrementally integrated into the existing map. The framework employs spatial and semantic correspondences, along with prior knowledge from the digital twin, to merge and synchronize asynchronous data collected by multiple robots. This process addresses challenges such as inaccurate object representations, class ambiguities, and data overlaps, while also capturing gradual or occasional environmental changes to ensure the digital twin accurately reflects real-world conditions. Experimental evaluations in representative office environments demonstrate the method’s effectiveness for scenarios involving moderate structural evolution, such as adding, moving, or deleting known objects. The results validate the framework’s practical relevance within its defined scope.</p>