De-noising Motion Predictions of Scuba Divers for Aquatic Robots
Authors
Published Date
Publisher
Type
Abstract
Current diver predictors output a sequence of bounding boxes, with two corners randomly sampled from two bivariate gaussians. This introduces noise and uncertainty into the prediction outputs and makes the conversion of this sequence of 2D boxes into a 3D motion vector challenging. This poster describes an approach to de-noise this output and convert the predictions into a format that can be used by aquatic robots to plan their motion and follow scuba divers robustly.
Description
Related to
item.page.replaces
License
Series/Report Number
Funding Information
This research was supported by the Undergraduate Research Opportunities Program (UROP).
item.page.isbn
DOI identifier
Previously Published Citation
Other identifiers
Suggested Citation
. (2021). De-noising Motion Predictions of Scuba Divers for Aquatic Robots. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/219530.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.