Constrained sampling of 2.5D probabilistic maps for augmented inference

© 2016 IEEE. This work exploits modeling spatial correlation in 2.5D data using Gaussian Processes (GPs), and produces constrained sampling realizations on these models to improve certainty in the predictions by means of integrating additional sparse information. Data organized in 2.5D such as elevation and thickness maps has been extensively studied in the fields of robotics and geostatistics. These maps are typically represented as a probabilistic 2D grid that stores an estimated value (height or thickness) for each cell. With the increasing popularity and deployment of robotic devices for infrastructure inspection, 2.5D data becomes a common interpretation of the condition of the target being inspected. Modeling the spatial dependencies and making inferences on new grid locations is a common task that has been addressed using GPs, but inference results on locations which are weakly correlated with the training data are generally not sufficiently informative and distinctly uncertain. The predictive capability of the proposed framework, which is applicable to any 2.5D data, is demonstrated with field inspection data from pipelines. Specifically, sparse and complementary measurements from alternative sensing modalities have been incorporated into the model to predict in more detail local thickness conditions where GP training data is limited. The output of this work aims to probabilistically present variations of the target in the case that both accuracy and reasonable diversity are of significant interest