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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