Juan José Cabrera

Marcos Alfaro, Juan José Cabrera, María Flores et al.

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.

Judith Vilella-Cantos, Juan José Cabrera, Luis Payá et al.

In autonomous navigation systems, the solution of the place recognition problem is crucial for their safe functioning. But this is not a trivial solution, since it must be accurate regardless of any changes in the scene, such as seasonal changes and different weather conditions, and it must be generalizable to other environments. This paper presents our method, MinkUNeXt-SI, which, starting from a LiDAR point cloud, preprocesses the input data to obtain its spherical coordinates and intensity values normalized within a range of 0 to 1 for each point, and it produces a robust place recognition descriptor. To that end, a deep learning approach that combines Minkowski convolutions and a U-net architecture with skip connections is used. The results of MinkUNeXt-SI demonstrate that this method reaches and surpasses state-of-the-art performance while it also generalizes satisfactorily to other datasets. Additionally, we showcase the capture of a custom dataset and its use in evaluating our solution, which also achieves outstanding results. Both the code of our solution and the runs of our dataset are publicly available for reproducibility purposes.