Carnegie Mellon University
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VANE
We are exploring a mix of physics-based and data driven high fidelity sensor modeling techniques. The goal is to develop a system that can provide much more realistic UGV simulation than current techniques. Such simulation will play a crucial role in speeding up the development cycle, and in validating platforms. Sponsored by the US ACE ERDC.
Vehicle Localization in Naturally Varying Environments
The purpose of this project is to develop methods for place matching that are invariant to short- and long-term environmental variations in support of autonomous vehicle localization in GPS-denied situations.
Very Rough Terrain Nonholonomic Trajectory Generation and Motion Planning for Rovers
We are developing rough terrain trajectory generation algorithms for local path planning and optimal regional motion planning methods using a constrained search space.
Visual SLAM for Industrial Robots
We are exploring algorithms to support visual mapping and localization for a robot vehicle operating in an industrial setting such as an LNG production plant. This work is sponsored by QNRF.
Visual Yield Mapping with Optimal and Generative Sampling Strategies
This research project aims to develop methods to automatically collect visual image data to infer, estimate and forecast crop yields -- producing yield maps with high-resolution, across large scales and with accuracy. To achieve efficiency and accuracy, statistical sampling strategies are designed for human-robot teams that are optimal in the number of samples, location of samples, cost of sampling and accuracy of crop estimates.
Xavier
Perceptual, reasoning and learning abilities in autonomous mobile robots