Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments
This work addresses robust robot navigation for agricultural applications, but it is incremental as it enhances existing descriptors with semantic integration.
The paper tackled the problem of visual feature matching in outdoor agricultural environments, specifically vineyards, where repetitive structures cause perceptual aliasing, and improved matching accuracy by 12.6% in tasks like relative pose estimation and visual localization.
Robust robot navigation in outdoor environments requires accurate perception systems capable of handling visual challenges such as repetitive structures and changing appearances. Visual feature matching is crucial to vision-based pipelines but remains particularly challenging in natural outdoor settings due to perceptual aliasing. We address this issue in vineyards, where repetitive vine trunks and other natural elements generate ambiguous descriptors that hinder reliable feature matching. We hypothesise that semantic information tied to keypoint positions can alleviate perceptual aliasing by enhancing keypoint descriptor distinctiveness. To this end, we introduce a keypoint semantic integration technique that improves the descriptors in semantically meaningful regions within the image, enabling more accurate differentiation even among visually similar local features. We validate this approach in two vineyard perception tasks: (i) relative pose estimation and (ii) visual localisation. Across all tested keypoint types and descriptors, our method improves matching accuracy by 12.6%, demonstrating its effectiveness over multiple months in challenging vineyard conditions.