论文标题
转向序列 - 萨克：一种基于序列的，无训练的视觉位置识别技术，用于改变环境
ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments
论文作者
论文摘要
Visual Place识别（VPR）是在不断变化的观点和外观下正确召回先前访问的地方的能力。存在大量手工制作和深度学习的VPR技术，前者遭受外观变化，后者具有巨大的计算需求。在本文中，我们提出了一种新的手工VPR技术，该技术在具有挑战性的条件下实现了最新的位置匹配性能。我们的技术结合了两种现有的无培训vpr技术，即seqslam和cohog，它们分别对条件和观点变化都有稳定。这种混合物，即交流的slam，利用顺序信息和块范围来处理外观变化，同时使用区域横向横向匹配来实现观点不变性。我们分析查询框架之间的内容 - 遍布层，以找到最小序列长度，同时还重新使用基于环境的序列长度调整的图像熵信息。与4个公共数据集上的8种当代VPR技术相比，最新的性能是相比之下。还提供了关于序列长度的定性见解和消融研究。
Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place under changing viewpoints and appearances. A large number of handcrafted and deep-learning-based VPR techniques exist, where the former suffer from appearance changes and the latter have significant computational needs. In this paper, we present a new handcrafted VPR technique that achieves state-of-the-art place matching performance under challenging conditions. Our technique combines the best of 2 existing trainingless VPR techniques, SeqSLAM and CoHOG, which are each robust to conditional and viewpoint changes, respectively. This blend, namely ConvSequential-SLAM, utilises sequential information and block-normalisation to handle appearance changes, while using regional-convolutional matching to achieve viewpoint-invariance. We analyse content-overlap in-between query frames to find a minimum sequence length, while also re-using the image entropy information for environment-based sequence length tuning. State-of-the-art performance is reported in contrast to 8 contemporary VPR techniques on 4 public datasets. Qualitative insights and an ablation study on sequence length are also provided.
