Refining the Fusion of Pepper Robot and Estimated Depth Maps Method for Improved 3D Perception

As it is well known, some versions of the Pepper robot provide poor depth perception due to the lenses it has in front of the tridimensional sensor. In this paper, we present a method to improving that faulty 3D perception. Our proposal is based on a combination of the actual depth readings of Pepper and a deep learning-based monocular depth estimation. As shown, the combination of both of them provides a better 3D representation of the scene. In previous works we made an initial approximation of this fusion technique, but it had some drawbacks. In this paper we analyze the pros and cons of the Pepper readings, the monocular depth estimation method and our previous fusion method. Finally, we demonstrate that the proposed fusion method outperforms them all.This work was supported in part by the Spanish Government, through Feder funds under Grant TIN2016-76515R, and in part by the Spanish Grant for Ph.D. studies under Grant ACIF/2017/243 and Grant FPU16/00887. The work of E. Cruz was supported by the Panamenian Grant for Ph.D. studies IFARHU & SENACYT under Grant 270-2016-207