Hierarchical place recognition with omnidirectional images and curriculum learning-based loss functions

TL;DR

This paper introduces a hierarchical visual place recognition method using panoramic images and curriculum learning-based triplet loss functions, improving robustness and accuracy in challenging environments for mobile robots.

Contribution

It proposes a novel curriculum learning-based triplet loss for deep models using panoramic images, enhancing discriminative feature learning for place recognition.

Findings

Outperforms standard contrastive loss in challenging conditions

Effective in indoor and outdoor environments with illumination changes

Robust against noise, occlusions, and limited training data

Abstract

This paper addresses Visual Place Recognition (VPR), which is essential for the safe navigation of mobile robots. The solution we propose employs panoramic images and deep learning models, which are fine-tuned with triplet loss functions that integrate curriculum learning strategies. By progressively presenting more challenging examples during training, these loss functions enable the model to learn more discriminative and robust feature representations, overcoming the limitations of conventional contrastive loss functions. After training, VPR is tackled in two steps: coarse (room retrieval) and fine (position estimation). The results demonstrate that the curriculum-based triplet losses consistently outperform standard contrastive loss functions, particularly under challenging perceptual conditions. To thoroughly assess the robustness and generalization capabilities of the proposed method, it is evaluated in a variety of indoor and outdoor environments. The approach is tested against common challenges in real operation conditions, including severe illumination changes, the presence of dynamic visual effects such as noise and occlusions, and scenarios with limited training data. The results show that the proposed framework performs competitively in all these situations, achieving high recognition accuracy and demonstrating its potential as a reliable solution for real-world robotic applications. The code used in the experiments is available at https://github.com/MarcosAlfaro/TripletNetworksIndoorLocalization.git.