预测视觉特征的长期稳健性

Benjamin Metka, Annika Besetzny, Ute Bauer-Wersing, M. Franzius
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引用次数: 3

摘要

许多基于视觉的定位方法提取局部视觉特征来构建环境的稀疏地图,并从特征对应中估计相机的位置。然而,大多数特征通常只能在短时间内检测到,因此地图中的大多数信息在较长时间内就会过时。因此,长期定位是一个具有挑战性的问题,特别是在室外场景中,由于不同的白天时间、天气条件或季节影响,环境的外观可能会发生巨大变化。我们建议从检测到的兴趣点周围的纹理和颜色信息中学习稳定和不稳定特征特征的模型,从而可以预测视觉特征的鲁棒性。该模型可以结合到传统的特征提取和匹配过程中,以在映射阶段拒绝潜在的不稳定特征。额外滤波步骤的应用产生了更紧凑的地图,因此减少了误报匹配的概率,误报匹配可能导致定位系统的完全失败。该模型使用跨季节在同一轨道上的火车旅行记录进行训练,这有助于识别稳定和不稳定的特征。在同一领域数据上的实验证明了所学习特征的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the long-term robustness of visual features
Many vision based localization methods extract local visual features to build a sparse map of the environment and estimate the position of the camera from feature correspondences. However, the majority of features is typically only detectable for short time-frames so that most information in the map becomes obsolete over longer periods of time. Long-term localization is therefore a challenging problem especially in outdoor scenarios where the appearance of the environment can change drastically due to different day times, weather conditions or seasonal effects. We propose to learn a model of stable and unstable feature characteristics from texture and color information around detected interest points that allows to predict the robustness of visual features. The model can be incorporated into the conventional feature extraction and matching process to reject potentially unstable features during the mapping phase. The application of the additional filtering step yields more compact maps and therefore reduces the probability of false positive matches, which can cause complete failure of a localization system. The model is trained with recordings of a train journey on the same track across seasons which facilitates the identification of stable and unstable features. Experiments on data of the same domain demonstrate the generalization capabilities of the learned characteristics.
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