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The Bayesian Computational Sensor Network methodology is applied to small-scale structural health monitoring. A mobile robot, equipped with vision and ultrasound sensors, maps smallscale structures for damage (e.g., holes, cracks) by localizing itself and the damage in the map. The combination of vision and ultrasound reduces the uncertainty in damage localization. The data storage and analysis takes place exploiting cloud computing mechanisms, and there is also an off-line computational model calibration component which returns information to the robot concerning updated on-board models as well as proposed sampling points. The approach is validated in a set of physical experiments.