Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911761026777088 |
|---|---|
| author | Françani, André O. Maximo, Marcos R. O. A. |
| author_facet | Françani, André O. Maximo, Marcos R. O. A. |
| contents | Deep learning algorithms have driven expressive progress in many complex tasks. The loss function is a core component of deep learning techniques, guiding the learning process of neural networks. This paper contributes by introducing a consistency loss for visual odometry with deep learning-based approaches. The motion consistency loss explores repeated motions that appear in consecutive overlapped video clips. Experimental results show that our approach increased the performance of a model on the KITTI odometry benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10857 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning Françani, André O. Maximo, Marcos R. O. A. Computer Vision and Pattern Recognition Robotics 68T45 68T07 Deep learning algorithms have driven expressive progress in many complex tasks. The loss function is a core component of deep learning techniques, guiding the learning process of neural networks. This paper contributes by introducing a consistency loss for visual odometry with deep learning-based approaches. The motion consistency loss explores repeated motions that appear in consecutive overlapped video clips. Experimental results show that our approach increased the performance of a model on the KITTI odometry benchmark. |
| title | Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning |
| topic | Computer Vision and Pattern Recognition Robotics 68T45 68T07 |
| url | https://arxiv.org/abs/2401.10857 |