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37‐3: Invited Paper: Deep‐Learning based Approaches to Visual‐Inertial Odometry for Autonomous Tracking Applications
Author(s) -
Me Harsh,
Ramachandrappa Aashik,
Kesinger Jake
Publication year - 2018
Publication title -
sid symposium digest of technical papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.351
H-Index - 44
eISSN - 2168-0159
pISSN - 0097-966X
DOI - 10.1002/sdtp.12603
Subject(s) - odometry , artificial intelligence , visual odometry , computer science , inertial frame of reference , computer vision , robotics , augmented reality , heuristics , deep learning , inertial measurement unit , robot , mobile robot , physics , quantum mechanics , operating system
Recent geometric approaches to visual‐inertial odometry have shown impressive accuracy with real‐time performance in autonomous tracking applications in several fields including virtual and augmented reality (VR & AR) as well as robotics. But these methods are still not robust to challenging conditions due to their dependence on hand‐engineered features, heuristics, sensor calibration and manual synchronization (when using visual and inertial sensors). In this paper, we review the recent advances in deep learning based approaches to odometry and identify some future research directions.