Shared Transformer Encoder with Mask-Based 3d Model Estimation for Container Mass Estimation

Tomoya Matsubara, Seitaro Otsuki, Yuiga Wada, Haruka Matsuo, Takumi Komatsu, Yui Iioka, Komei Sugiura, Hideo Saito

Abstract: For human-safe robot control in human-to-robot handover, the physical properties of containers and fillings should be accurately estimated. In this paper, we propose a Transformer encoder that shares the same architecture and parameters for filling level and type estimation. We also propose a mask-based geometric algorithm to estimate 3D models of containers for the estimation of their capacity and dimensions. We further use these estimations to estimate their mass in a Convolutional Neural Network model. Experiments show that our Transformer model produced encouraging results in both estimations. While challenges remain in our mask-based algorithm and Convolutional Neural Network model, their results revealed several ways for improvement.