Aaron Wilhelm

GT-BoW is a geometry-aware image-retrieval method for ground-texture localization. It combines an approximate k-means vocabulary and soft assignment with constraints derived from the fixed scale and consistent orientation of downward-facing images. Separate high-accuracy and high-speed variants address the different demands of global localization and loop-closure detection. On the full benchmark, the method increases global-localization mean average precision from 0.026 to 0.559 and detects nearly 3× as many loop closures as a standard DBoW baseline.

Pruning weight helps growers assess vine vigor and plan vineyard management, but conventional measurement methods are labor-intensive or require specialized equipment. This work presents an affordable computer-vision method that combines a smartphone camera with structured light. It achieves R² = 0.80 on vertical-shoot-positioned vines, outperforming prior vision-based approaches, and reports initial results (R² = 0.29) for high-cordon vines, a training system not previously evaluated with computer vision.

L-GROUT is a lightweight global-localization system that matches natural ground texture observed by a downward-facing camera to a prebuilt map. Its main contributions are improved database-feature selection, locality-preserving dimensionality reduction for fast binary features, and a spatial-voting stage that retains accuracy in lightweight configurations. The complete system produces millimeter-scale estimates at more than 4 Hz on a Raspberry Pi 4, without GPU acceleration or environmental modification.