Deployed systems in many technical areas including object-detection, scene segmentation, localization, tracking, mapping, machine-learning, phenomics/genotype prediction, deep convolutional neural networks.

*Latest March 2019* Link to Abundant Robotics press release on commercial deployment of apple harvester

Link to

Breeding High Yielding Bioenergy
Project Website
Integrate agriculture, information technology, and engineering communities to design and apply new tools to the development of improved varieties of energy sorghum, a crop used to produce biofuel. Producing the large amounts of biomass needed for biofuels to displace petroleum requires significant improvements to the productivity and efficiency of biofuel crops. Research enhances methods for crop phenotyping (identifying and measuring the physical characteristics of plants), which are currently time-intensive and imprecise. The new approaches will include automated methods for observing and recording characteristics of plants and advanced algorithms for analyzing data and predicting plant growth potential. These innovations will accelerate the annual yield gains of traditional plant breeding and support the discovery of new crop traits that improve water productivity and nutrient use efficiency.

Autonomous Vineyard and Orchard Mapping
Project Website
Vineyard and orchard 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.

Tracking for Helicopter Landing
Project Website
This research is to to detect landing sites for helicopters in real-time from onboard cameras.

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.

Generative 3D Surface and Lighting Models

A novel localization technique that explicitly incorporates a light model and demonstrated the ability to localize an autonomous submarine to navigate underwater structures. The underwater structures such as oil-rigs and pipelines are curved and have few distinguishing features in terms of visual appearance. Further, due to the onboard lighting, the appearance changed drastically based on the relative pose of the vehicle. The system uses a light model to render realistic synthetic images of the environment to compare with the real camera images. Visual localization systems rarely have used a light model to predict the appearance of the scene. The system developed fully respects and accounts for appearance changes to enable successful localization in conditions that are problematic for conventional methods that try to factor-out the lighting conditions.