Ryan W. Wolcott

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This paper reports on the problem of map-based visual localization in urban environments for autonomous vehicles. Self-driving cars have become a reality on roadways and are going to be a consumer product in the near future. One of the most significant road-blocks to autonomous vehicles is the prohibitive cost of the sensor suites necessary for(More)
This paper reports on a fast multiresolution scan matcher for vehicle localization in urban environments for self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a three-dimensional (3D) light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these(More)
This paper reports on a Naval Engineering Education Center (NEEC) design-build-test project focused on the development of a fully autonomous system for landing Navy unmanned aerial vehicles (UAVs) on transiting ships at sea. Our NEEC team of engineering students researched image processing techniques, estimation frameworks, and control algorithms to(More)
This paper reports on visual obstacle detection from a monocular camera for autonomous vehicles. By leveraging a textured prior map, we propose a probabilistic formulation for finding the optimal image partition that separates obstacles from groundplane. Our key insight is the use of a prior map that enables ground appearance models conditioned on prior map(More)
Many autonomous systems require the ability to perceive and understand motion in a dynamic environment. We present a novel algorithm that estimates this motion from raw LIDAR data in real-time without the need for segmentation or model-based tracking. The sensor data is first used to construct an occupancy grid. The foreground is then extracted via a(More)
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