Summary of An Evaluation Of Cnn Models and Data Augmentation Techniques in Hierarchical Localization Of Mobile Robots, by J.j. Cabrera et al.
An evaluation of CNN models and data augmentation techniques in hierarchical localization of mobile robots
by J.J. Cabrera, O. J. Céspedes, S. Cebollada, O. Reinoso, L. Payá
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper evaluates the effectiveness of Convolutional Neural Network (CNN) models and data augmentation techniques in localizing a mobile robot using omnidirectional images. The authors conduct an ablation study on different state-of-the-art CNN backbones and propose various visual effects for data augmentation to improve the visual localization of the robot. The proposed method involves adapting and re-training a CNN with two purposes: rough localization and fine localization. The paper assesses the impact of ConvNeXt, a state-of-the-art CNN model, on the proposed localization task. Additionally, the authors evaluate the performance of the resulting CNNs under real operating conditions, including changes in lighting conditions. The code is publicly available for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots find their way around buildings using special images called omnidirectional images. It compares different types of computer vision models and ways to improve training data to see which works best. The goal is to have the robot quickly figure out where it is in a building, then get even more precise details about its location. The authors test how well these methods work in real-world situations with changing lighting conditions. They share their code so others can build on this research. |
Keywords
» Artificial intelligence » Cnn » Data augmentation » Neural network
