Multiview triangulation with uncertain data

The traditional triangulation algorithms in multiview geometry problems have the drawback that its solution is locally optimal. Robust Optimization is a specific and relatively novel methodology for handling optimization problems with uncertain data. The key idea of robust optimization is to find the best possible performance in the worst case. In this paper, we propose a novel approach which solves the triangulation problems with perturbational data employing robust optimization. The main advantage of this method is global optimality under the perturbational data. Good performance has been demonstrated by experimental results for synthetic and real data, respectively.