In this study, we address the critical challenge of balancing speed and accuracy while maintaining interpretablity in visual odometry (VO) systems, a pivotal aspect in the field of autonomous navigation and robotics. Traditional VO systems often face a trade-off between computational speed and the precision of pose estimation. To tackle this issue, we introduce an innovative system that synergistically combines traditional VO methods with a specifically tailored fully connected network (FCN). Our system is unique in its approach to handle each degree of freedom independently within the FCN, placing a strong emphasis on causal inference to enhance interpretability. This allows for a detailed and accurate assessment of relative pose error (RPE) across various degrees of freedom, providing a more comprehensive understanding of parameter variations and movement dynamics in different environments. Notably, our system demonstrates a remarkable improvement in processing speed without compromising accuracy. In certain scenarios, it achieves up to a 5% reduction in Root Mean Square Error (RMSE), showcasing its ability to effectively bridge the gap between speed and accuracy that has long been a limitation in VO research. This advancement represents a significant step forward in developing more efficient and reliable VO systems, with wide-ranging applications in real-time navigation and robotic systems.

翻译：在本研究中，我们探讨了视觉里程计（VO）系统中平衡速度与准确性并保持可解释性的关键挑战——这是自主导航与机器人领域的关键问题。传统VO系统常在计算速度与姿态估计精度之间面临权衡。为解决此问题，我们提出一种创新系统，该体系协同融合传统VO方法与定制化全连接网络（FCN）。我们的系统独特之处在于：在FCN内独立处理每个自由度，着重强调因果推断以增强可解释性。这使得系统能够细致准确地评估不同自由度下的相对位姿误差（RPE），从而更全面地理解不同环境中的参数变化与运动动力学。值得注意的是，本系统在保持精度的同时显著提升了处理速度。在特定场景下，其均方根误差（RMSE）降低幅度可达5%，有效弥合了长期制约VO研究发展的速度-精度鸿沟。该突破为开发更高效可靠的VO系统迈出关键一步，在实时导航与机器人系统中具有广泛应用前景。