Diffusion-Based Completion for Multi-Robot Active Scene Reconstruction Towards IoT Applications

Autonomous reconstruction of unknown scenes using multiple robots acting as mobile Internet of Things (IoT) nodes becomes a fundamental capability for extensive IoT applications, such as environmental monitoring, and search and rescue. However, existing multi-robot autonomous reconstruction approaches still suffer from incomplete observations, redundant generated coverage tasks, and overly distant assigned tasks. To this end, we first utilize a diffusion-based model for object completion in autonomously reconstructed scenes using 3D Gaussian Splatting, and obtain Gaussian mixture model-based object uncertainties to guide the robots in generating more accurate scanning tasks that fill holes and enhance reconstruction quality. We then design an efficient task filtering mechanism that utilizes clustering frontiers and exploration tasks, as well as instances and reconstruction tasks, enabling the elimination of redundant coverage tasks. We also devise a task reassignment mechanism for robots based on the required travel costs to avoid unnecessary detours, which further improves scanning efficiency. Extensive experimental results show that our method exhibits higher reconstruction quality and superior planning efficiency compared to existing multi-robot autonomous reconstruction methods.