Illumination control in view of dynamic (re)planning of 3D reconstruction tasks

Accuracy of 3D vision-based reconstruction tasks depends both on the complexity of analyzed objects and on good viewing / illumination conditions, ensuring image quality and minimizing consequently measurement errors after processing of acquired images. In this contribution, as a complement to an autonomous cognitive vision system automating 3D reconstruction and using Situation Graph Trees (SGTs) as a planning / control tool, these graphs are optimized in two steps. The first (off-line) step addresses the placement of lighting sources, with the aim to find positions minimizing processing errors during the subsequent reconstruction steps. In the second step, on-line application of the SGT-based control module focuses on adjustment of illumination conditions (e. g., intensity), leading eventually to process re-planning, and enabling further to extract optimally the contour data required for 3D reconstruction. The whole illumination optimization procedure has been fully automated and included in the dynamic (re-)planning tool for visionbased reconstruction tasks, e. g. in view of quality control applications.