Features play an important role in various visual tasks, especially in visual place recognition applied to perceptually changing environments. We address challenges in place recognition due to dynamic and confusable patterns by proposing a discriminative and semantic feature selection network named DSFeat in this study. With supervision of both semantic information and attention mechanism, the pixel-wise stability of features can be estimated, which indicates the probability of a static and discriminative region where features are extracted. We can then select features that are insensitive to dynamic interference and distinguishable for correct matching. The designed feature selection model is evaluated in place recognition and SLAM system using several public datasets with varying appearances and viewpoints. Experimental results demonstrate the effectiveness of the proposed method. Note that our proposed method can be easily integrated into any feature-based SLAM system.

特征在各种视觉任务中发挥着重要作用，尤其是在应用于感知变化环境的视觉位置识别中。我们通过在本研究中提出一个名为 DSFeat 的判别性和语义特征选择网络来解决由于动态和易混淆的模式而导致的现场识别挑战。在语义信息和注意力机制的监督下，可以估计特征的像素稳定性，这表明提取特征的静态和判别区域的概率。然后我们可以选择对动态干扰不敏感且可区分以进行正确匹配的特征。设计的特征选择模型使用具有不同外观和视点的多个公共数据集在地点识别和 SLAM 系统中进行评估。实验结果证明了所提出方法的有效性。请注意，我们提出的方法可以轻松集成到任何基于特征的 SLAM 系统中。