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.

翻译：深度学习算法已在许多复杂任务中取得了显著进展。损失函数作为深度学习技术的核心组成部分，引导着神经网络的学习过程。本文提出了一种基于深度学习方法的视觉里程计一致性损失。该运动一致性损失利用了连续重叠视频片段中出现的重复运动模式。实验结果表明，我们的方法在KITTI里程计基准测试中提升了模型的性能表现。