2023/12/20 by Habib Boloorchi Tabrizi, Tabrizi, Habib Boloorchi, Christopher Crick +1
Computer Science · Medicine · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Retinal Imaging and Analysis #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2312.13162
openalex publication_date 2023/12/20 · openalex created_date 2023/12/22 · openalex updated_date 2026/07/28
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.