Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach

TL;DR

This paper presents a brain-inspired visual odometry system that balances computational speed and interpretability by combining traditional methods with a tailored neural network, achieving faster processing with improved accuracy.

Contribution

It introduces a novel system integrating traditional VO with a fully connected network that handles each degree of freedom independently for better interpretability and performance.

Findings

Up to 5% reduction in RMSE in certain scenarios

Enhanced interpretability through causal inference

Faster processing without sacrificing accuracy

Abstract

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.