Autonomy for Characterization of Hydrodynamic Environments from Infrared (IR) Imagery
Abstract
Our research collaborator for this ONR project, SRI International (SRI) developed a proof of concept wherein the surface velocity and bathymetry for riverine environments are estimated using image processing techniques and hydrodynamic inverse modeling applied to Infrared (IR) video imagery of the water surface, collected by a small unmanned aerial system (UAS) platform. As camera stability has been a large source of uncertainty for autonomous navigation, bathymetry estimates, and wave motion models, Caltech will develop methodologies and algorithms in order to provide robust land/river segmentation and camera stabilization. The unique challenges present in riverine environment and IR images require more unconventional approaches to aid in monocular-based Simultaneous Localization and Mapping (SLAM) navigation. These challenges lend themselves to algorithmic and machine learning enhancements that have previously been done for other similarly difficult environments at Caltech. Further, the existing work done on previous data, including Particle Image Velocimetry (PIV) surface current estimation from SRI and wave motion models done at NRL, can be tightly fused to existing SLAM frameworks to enhance robustness and stability, as well as potentially enabling autonomous navigation in previously unmanageable environments.Approved for Public Release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 09, 2021
- Source ID
- N000142112374
Entities
People
- Soon-Jo Chung
Organizations
- California Institute of Technology
- Office of Naval Research
- United States Navy