(PREPRINT) Hierarchical localization with panoramic views and triplet loss functions
M. Alfaro, J. J. Cabrera, L. M. Jimenez, O. Reinoso and L. Payá
Artificial Intelligence Journal   (2024)
Ed. Elsevier  DOI: https://doi.org/10.48550/arXiv.2404.14117  BIBTEX:@article{alfaro2024hierarchical, title={Hierarchical localization with panoramic views and triplet loss functions}, author={Alfaro, Marcos and Cabrera, Juan Jos{'e} and Jim{'e}nez, Luis Miguel and Reinoso, {'O}scar and Pay{'a}, Luis}, journal={arXiv preprint arXiv:2404.14117}, year={2024} }

Abstract:

The main objective of this paper is to address the mobile robot localization problem with Triplet Convolutional Neural Networks and test their robust ness 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 local ization 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.