Deeply-Integrated Feature Tracking for Embedded Navigation

Abstract : The Air Force Institute of Technology is investigating techniques to improve aircraft navigation using low-cost imaging and inertial sensors. Stationary features tracked within the image are used to improve the inertial navigation estimate. Features are tracked using a correspondence search between frames. Previous research investigated aiding these correspondence searches using inertial measurements. While this research demonstrated the benefits of further sensor integration, it still relied on robust feature descriptors to obtain a reliable correspondence match in the presence of rotation and scale changes. Unfortunately, these robust feature extraction algorithms are computationally intensive and require significant resources for real-time operation. Simpler feature extraction algorithms are much more efficient, but their feature descriptors are not invariant to scale, rotation, or affine warping which limits matching performance during arbitrary motion. This research uses inertial measurements to predict not only the location of the feature in the next image but also the feature descriptor, resulting in robust correspondence matching with low computational overhead.

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