无需模型估计，用深度特征重排序提升长期视觉定位精度
On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features
- 基于深度局部特征的空间匹配实现无模型重排序
- 在多个公开数据集上达到当前顶尖水平性能
- 特别适合长期自主系统中的外观变化鲁棒性需求
重排序是视觉位置识别的第二阶段，系统从预选候选集中选出最佳匹配图像。无模型方法通过空间比较对应局部视觉特征来计算图像对相似性，避免了耗时的图像间变换建模。本文聚焦于基于标准局部视觉特征的无模型重排序，特别针对深度学习提取的局部特征在长期自主系统中的适用性。提出三种新方法，专为深度学习局部特征设计，这些特征对各种外观变化具有高度鲁棒性，对长期自主系统至关重要。所有方法与D2-net特征检测器（Dusmanu, 2019）结合，在多个具有挑战性的公开数据集上进行了实验验证。结果表明，性能与当前最先进方法相当，证实无模型方法是长期视觉位置识别中一条可行且值得探索的路径。
原文摘要 · Abstract (English)
Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity based on a spatial comparison of corresponding local visual features, eliminating the need for computationally expensive estimation of a model describing transformation between images. The article focuses on model-free re-ranking based on standard local visual features and their applicability in long-term autonomy systems. It introduces three new model-free re-ranking methods that were designed primarily for deep-learned local visual features. These features evince high robustness to various appearance changes, which stands as a crucial property for use with long-term autonomy systems. All the introduced methods were employed in a new visual place recognition system together with the D2-net feature detector (Dusmanu, 2019) and experimentally tested with diverse, challenging public datasets. The obtained results are on par with current state-of-the-art methods, affirming that model-free approaches are a viable and worthwhile path for long-term visual place recognition.
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