This paper overviews different pose representations and metric functions in visual odometry (VO) networks. The performance of VO networks heavily relies on how their architecture encodes the information. The choice of pose representation and loss function significantly impacts network convergence and generalization. We investigate these factors in the VO network DeepVO by implementing loss functions based on Euler, quaternion, and chordal distance and analyzing their influence on performance. The results of this study provide insights into how loss functions affect the designing of efficient and accurate VO networks for camera motion estimation. The experiments illustrate that a distance that complies with the mathematical requirements of a metric, such as the chordal distance, provides better generalization and faster convergence. The code for the experiments can be found at https://github.com/remaro-network/Loss_VO_right

本研究重点概述了视觉里程计（VO）网络中的不同姿态表示和度量函数。通过在VO网络DeepVO中实现基于欧拉角、四元数和弦距离的损失函数并分析其对性能的影响，我们研究了姿态表示和损失函数对网络收敛和泛化的显著影响。实验结果表明，符合度量要求的距离（如弦距离）可以提供更好的泛化能力和更快的收敛速度，为相机运动估计的高效准确设计提供了见解。

正确损失：基于学习的视觉里程计中的欧几里得和黎曼度量