EVALUATION OF A SLAM-BASED POINT CLOUD FOR DEFLECTION ANALYSIS IN HISTORIC TIMBER FLOORS
Keywords: Wearable Mobile Mapping, Diagnostics, Timber floor, Simultaneous Location and Mapping, Deep Learning, Noise reduction
Abstract. This paper aims at evaluating the possibility of using wearable mobile mapping solutions as a tool for detecting deflections in timber floors. These construction systems are prone to present this type of damage due to the mechanical properties of the wood (relatively low flexural stiffness and creep behaviour). During this study we have evaluated the chance of introducing an additional stage to the general workflow. This stage is devoted to reduce the noise of the 3D point cloud by using the Statistical Outlier Removal filter in combination with a noise-reduction filter such as the Anisotropic filter, the PointCleannet or the Scored-based denoised networks (Deep Learning methods). According with our results, the use of this strategies improves the quality of the 3D point cloud form a qualitative and quantitative point of view. However, these improvements seem to be not sufficient for using this product as a universal source of information for deflection analysis. In this sense, and according with the sensor and study case exploited, this type of point clouds could be used in floors with 5-8-meter length and a relative deflection of about L/200 or higher.