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arXiv · 2401.10857

Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning

André O. Françani·Marcos R. O. A. Maximo
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Abstract

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.

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André O. Françani, Marcos R. O. A. Maximo. 2024-01-19. Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning. https://doi.org/10.1109/lars%2Fsbr%2Fwre59448.2023.10332921

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