Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments

2025-03-11

AI Summary

To address visual feature matching ambiguities in outdoor orchards (e.g., vineyards) caused by repetitive trunk structures and seasonal appearance variations, this paper proposes a semantic-enhanced keypoint matching method that requires no retraining of the backbone network. Our approach dynamically integrates lightweight semantic segmentation cues into local descriptor generation via semantic-guided keypoint region weighting and multi-scale feature alignment, thereby enhancing descriptor discriminability. Evaluated on a cross-month real-world vineyard dataset, the method achieves an average 12.6% improvement in matching accuracy for relative pose estimation and visual localization tasks, outperforming mainstream methods including SIFT and SuperPoint. Moreover, it demonstrates robustness under challenging illumination conditions and across vegetation growth cycles.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: State Estimation
• Search and Optimization: Local Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Improve feature matching in outdoor agricultural environments.
• Address perceptual aliasing in vineyards with repetitive structures.
• Enhance keypoint descriptor distinctiveness using semantic information.

Innovation

• Keypoint semantic integration enhances descriptor distinctiveness.
• Improves matching accuracy in repetitive outdoor environments.
• Validated in vineyard pose estimation and visual localization.