  The main objective of this paper is to address the mobile robot localization
problem with Triplet Convolutional Neural Networks and test their robustness
against changes of the lighting conditions. We have used omnidirectional images
from real indoor environments captured in dynamic conditions that have been
converted to panoramic format. Two approaches are proposed to address
localization by means of triplet neural networks. First, hierarchical
localization, which consists in estimating the robot position in two stages: a
coarse localization, which involves a room retrieval task, and a fine
localization is addressed by means of image retrieval in the previously
selected room. Second, global localization, which consists in estimating the
position of the robot inside the entire map in a unique step. Besides, an
exhaustive study of the loss function influence on the network learning process
has been made. The experimental section proves that triplet neural networks are
an efficient and robust tool to address the localization of mobile robots in
indoor environments, considering real operation conditions.
