The Augmented-Reality Virtual At Sea Trainer (AR-VAST) allows SWO training for difficult situations such as small craft swarm attacks. The goal of AR-VAST is to provide the Navy and Marine Corps with training capabilities to defend against small boat attacks and for operations in littoral environments.
“Training on crew served weapons on coalition ships has not advanced to meet this new threat,” according to Lt. Nate Conger’s thesis abstract regarding AR-VAST. “The creation of a prototype Augmented Reality Virtual At Sea Train shows that emerging technologies in augmented reality systems can be used to create a low cost effective answer to this training need.”
Utilizing computer vision techniques on sonar images to estimate velocity of an Autonomous Underwater Vehicle
The REMUS autonomous underwater vehicle uses a high defination forward looking sonar for obstacle avoidance. I am attempting to use vision and optical flow techniques to estimate the velocity of the vehicle in the forward and lateral directions between sequential sonar images, like the one pictured below.
Platform Camera Aircraft Detection for Approach Evaluation and Training
Approach training currently relies solely on manual observation and verbal feedback to the pilot. This project aims to provide both pilots and Landing Signal Officers (LSOs) with valuable information about individual approaches in the carrier landing environment. The project will investigate fully automatic data acquisition by means of computer vision-based analysis of platform camera video. The obtained data supports enhanced LSO training, real-time approach analysis, and pilot self-improvement through advanced review capabilities.
There are many ways to describe human body postures and gestures in the various related fields of human-computer interaction, psychology, computer graphics and animation, motion capture, and so forth. This research investigates how these different fields can directly benefit from each other's advances by standardizing how vocabulary is introduced into a language that can serve all related disciplines.
Performing computer vision on PCs is extremely power hungry. Embedded vision systems achieve greater efficiency and promise mobility, possibly even higher performance. We are currently exploring these opportunities for various applications, including robot vision, networked camera systems, and vision for handheld devices.
Past Projects
