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Automatic Feature Tracking on Small Bodies for Autonomous Approach

Show simple item record Morrell, Benjamin J Villa, Jacopo Havard, Alexei 2021-10-29T21:07:37Z 2021-10-29T21:07:37Z 2020-11-16
dc.identifier.citation AIAA ASCEND 2020, Las Vegas, Nevada, November 16-18, 2020
dc.identifier.clearanceno CL#20-1795
dc.description.abstract Abstract—The autonomous approach of a spacecraft to an asteroid or comet (a small body) relies heavily on visual feature tracking to aid in estimating relative trajectories and the properties of the small body. Feature tracking for small bodies brings several challenges, including changing lighting, poor visual texture, and a concentration of features in a small part of an image. Six existing, open-source algorithms for feature tracking were tested on a simulated dataset and compared to the ground truth in the path of features. The main finding is that none of the algorithms provide all of the desired characteristics of long feature tracks with low errors and few outliers. Instead, there is a trade-off between long feature tracks and low error. The feature-matching algorithms SIFT, and BRISK provide good error characteristics, but short feature tracks, whereas the optical flow algorithm KLT provides long feature tracks, but with many features of large error. Given the challenges in feature tracking, it is recommended to focus development on each component of a feature tracking system: detection, description, and outlier rejection.
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2020
dc.title Automatic Feature Tracking on Small Bodies for Autonomous Approach
dc.type Preprint

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