My research interests are with the design of computer vision systems practical for field robotic applications. Computer vision, although a very promising perceptual tool, has been under-utilized in field robotics because of the difficulties creating reliable systems. It is the harsh nature of the environments that is especially challenging for computer vision systems. There are field robotic applications where computer vision is the only feasible perceptual mechanism due to its small size, low weight, long working range, and the rich nature of the information it provides. Therefore, it is essential to keep researching ways to improve the performance of computer vision systems under challenging conditions in order to further progress the development of field robotics.

Currently working on two main projects, river mapping and yield estimation in vineyards. Below are some links, videos and summaries of these two projects.

Autonomous Vineyard Canopy and Yield Estimation
Project Website
Vineyard managers want to know the state of their vines -- both the size of the vine canopy and the predicted harvest yield. Such information can be used to manage vine vegetative and reproductive growth to improve the efficiency of vineyard operations. Traditional industry practices for gathering crop and canopy estimates are labor-intensive, expensive, destructive, imprecise, spatially coarse and do not scale to large vineyards. The research project aims to design and demonstrate new sensor technologies for autonomously gathering crop and canopy size estimates from a vineyard -- expediently, precisely, accurately and at high-resolution -- with the goal to improve vineyard efficiency by enabling producers to measure and manage the principal components of grapevine production on an individual vine basis.
River Mapping
Project Website
This project is developing technology to map riverine environments from a low-flying rotorcraft. Challenges include dealing with varying appearance of the river and surrounding canopy, intermittent GPS and a highly constrained payload. We are developing self-supervised algorithms that can segment images from onboard cameras to determine the course of the river ahead, and we are developing devices and methods capable of mapping the shoreline.
Tracking for Helicopter Landing
Project Website
In this project we develop ship deck tracking and landing sensors and algorithms. These algorithms enable detection and tracking of a landing zone on a ship deck without external infrastructure. The algorithms track the visual and/or 3D appearance of the ship to compute a 6 degree of freedom position, orientation, and velocities. The challenges are to determine the position and orientation of the deck with high accuracy, and with disturbances such as obstacles and the moving landing zone.