キーポイントの意味統合による屋外農業環境での特徴マッチング改善
Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments
ブドウ園のような反復構造の多い屋外環境で、キーポイントに意味情報を統合して特徴記述子を識別しやすくし、特徴マッチング精度を12.6%向上させた。
著者: Rajitha de Silva, Jonathan Cox, Marija Popovic, Cesar Cadena, Cyrill Stachniss, Riccardo Polvara
分類: cs.CV, cs.RO
原文アブストラクト
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.
