  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.
