Sparse estimation under saturated condition for 3D Measurement and its application for bin-picking robot

Accurate and robust 3D measurement is widely required by industrial robot usage; however, 3D measurement under complex lighting conditions such that scenes include metallic objects or semi-transparent objects is still a difficult problem. We tackled this problem by using the Light Transport Matrix (LTM) sparse estimation. LTM is one of the representations of reflection on a projector-camera system, and by using LTM and epipolar geometry, the direct components of the reflection can be extracted. We proposed an extension of LTM estimation which enables sparse estimation to success under saturated condition. Our method utilizes Alternating Direction Method of Multipliers (ADMM) for both of the norm function and the saturation function. The key idea is to separate saturated part of the observation model from the original i\ minimization formulation. We also demonstrate that our measurement method performs well for robot visions by integrating to an actual robot task.