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.

翻译：户外环境中的鲁棒机器人导航需要能够处理重复结构和外观变化等视觉挑战的精确感知系统。视觉特征匹配对基于视觉的流程至关重要，但由于感知混淆，在自然户外场景中尤其具有挑战性。我们在葡萄园环境中解决这一问题，其中重复的葡萄藤主干和其他自然元素会产生阻碍可靠特征匹配的模糊描述符。我们假设与关键点位置关联的语义信息可通过增强关键点描述符的区分度来缓解感知混淆。为此，我们提出一种关键点语义集成技术，该技术能改善图像内具有语义意义区域的描述符，即使对于视觉上相似的局部特征也能实现更精确的区分。我们在两个葡萄园感知任务中验证了该方法：(i) 相对位姿估计与 (ii) 视觉定位。在所有测试的关键点类型和描述符中，我们的方法将匹配准确率提高了12.6%，证明了其在具有挑战性的葡萄园环境中跨越多个月份的有效性。