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

• URL: http://arxiv.org/abs/2312.13162v1
• Date: Wed, 20 Dec 2023 16:23:48 GMT
• Title: Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach
• Authors: Habib Boloorchi Tabrizi, Christopher Crick
• Abstract summary: This study addresses the challenge of balancing speed and accuracy in visual odometry (VO) systems. Traditional VO systems often face a trade-off between computational speed and the precision of pose estimation. Our system is unique in its approach to handle each degree of freedom independently within the fully connected network (FCN)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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