Localization of Cross-Junctions in Warehouse Beam Structure by Supervised Descent Method

Abstract: A new application of the Supervised Descent Method (SDM) [26] optimization algorithm in order to find solutions for modeling a structured environment such as a warehouse is investigated in this work. For modeling a structured warehouse, a large number of front-view images of a warehouse are collected. This work investigates basic computational elements for building a two-dimensional map of the warehouse by the SDM algorithm suggesting to use a well-known technique as feature extraction, i.e. Scale Invariant Feature Transform (SIFT) [16]. The ground-truths are extracted manually on pillar-beam intersections from real-world warehouse images. To address the problem of modeling a warehouse, different modeling scenarios ranging from a complex to a simple model each with increasing the initial suggested displacement are investigated. As an important contribution, this work reports statistics concerning the divergence rate of SDM (combined with SIFT) performance in all scenarios for both sides of corridors of the warehouse images. This work has shown that the SDM transformation method in its original form is not sufficient enough to be used in general visual object location problems.