Aaron Wilhelm

Ground texture localization using a downward-facing camera offers a low-cost, high-precision localization solution that is robust to dynamic environments and requires no environmental modification. We present a significantly improved bag-of-words (BoW) image retrieval system for ground texture localization, achieving substantially higher accuracy for global localization and higher precision and recall for loop closure detection in SLAM. Our approach leverages an approximate k-means (AKM) vocabulary with soft assignment, and exploits the consistent orientation and constant scale constraints inherent to ground texture localization. Identifying the different needs of global localization vs. loop closure detection for SLAM, we present both high-accuracy and high-speed versions of our algorithm. We test the effect of each of our proposed improvements through an ablation study and demonstrate our method’s effectiveness for both global localization and loop closure detection.

Pruning weight is indicative of a vine’s ability to produce crop the following year, informing vineyard management. Current methods for estimating pruning weight are costly, laborious, and/or require specialized know-how and equipment. In this paper we demonstrate an affordable, simple, computer vision-based method to measure pruning weight using a smartphone camera and structured light which produces results better than state-of-the-art techniques for vertical shoot position (VSP) vines and demonstrate initial steps towards estimating pruning weight in high cordon procumbent (HC) vines such as Concord. The simplicity and affordability of this technique lends its self to deployment by farmers today or on future viticulture robotics platforms. We achieved an R2=.80 for VSP vines (better than state-of-the-art computer vision-based methods) and R2=.29 for HC vines (not previously attempted with computer vision-based methods).

We present a lightweight ground texture based localization algorithm (L-GROUT) that improves the state of the art in performance and can be run in real-time on single board computers without GPU acceleration. Such computers are ubiquitous on small indoor robots and thus this work enables high-precision, millimeter-level localization without instrumenting, marking, or modifying the environment. The key innovations are an improved database feature extraction algorithm, a dimensionality reduction method based on locality preserving projections (LPP) that can accommodate faster-to-compute binary features, and an improved spatial filtering step that better preserves performance when the databases are tuned for lightweight applications. We demonstrate the approach by running the whole system on a low-cost single board computer (Raspberry Pi 4) to produce global localization estimates at greater than 4Hz on an outdoor asphalt dataset.