21 May 2015 Information surfing with the JHU/APL coherent imager
Author Affiliations +
Abstract
The ability to perform remote forensics in situ is an important application of autonomous undersea vehicles (AUVs). Forensics objectives may include remediation of mines and/or unexploded ordnance, as well as monitoring of seafloor infrastructure. At JHU/APL, digital holography is being explored for the potential application to underwater imaging and integration with an AUV. In previous work, a feature-based approach was developed for processing the holographic imagery and performing object recognition. In this work, the results of the image processing method were incorporated into a Bayesian framework for autonomous path planning referred to as information surfing. The framework was derived assuming that the location of the object of interest is known a priori, but the type of object and its pose are unknown. The path-planning algorithm adaptively modifies the trajectory of the sensing platform based on historical performance of object and pose classification. The algorithm is called information surfing because the direction of motion is governed by the local information gradient. Simulation experiments were carried out using holographic imagery collected from submerged objects. The autonomous sensing algorithm was compared to a deterministic sensing CONOPS, and demonstrated improved accuracy and faster convergence in several cases.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher R. Ratto, Kara R. Shipley, Nathaniel Beagley, Kevin C. Wolfe, "Information surfing with the JHU/APL coherent imager", Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94541F (21 May 2015); doi: 10.1117/12.2176338; https://doi.org/10.1117/12.2176338
PROCEEDINGS
15 PAGES


SHARE
Back to Top