Abstract
ABSTRACT This study presents a complete solution for multi-modal inspection of industrial components, including a processing pipeline for registering consecutive multi-modal point clouds comprising thermal and visible sensors’ data. A comparative evaluation of optimisation and learning-based registration methods is provided as part of the processing pipeline. Moreover, a benchmark dataset of point cloud data from different FOVs of industrial and construction component samples is provided (LeManchot-Points), having data from five point clouds with depth, colour and thermal information at each point. The experimental campaign with different objects demonstrates the proposed solution’s applicability for the multi-modal inspection of industrial components.
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