This paper presents an approach for creating a visual place recognition (VPR) database for localization in indoor environments from RGBD scanning sequences. The proposed approach is formulated as a minimization problem in terms of dominating set algorithm for graph, constructed from spatial information, and referred as DominatingSet. Our algorithm shows better scene coverage in comparison to other methodologies that are used for database creation. Also, we demonstrate that using DominatingSet, a database size could be up to 250-1400 times smaller than the original scanning sequence while maintaining a recall rate of more than 80% on testing sequences. We evaluated our algorithm on 7-scenes and BundleFusion datasets and an additionally recorded sequence in a highly repetitive office setting. In addition, the database selection can produce weakly-supervised labels for fine-tuning neural place recognition algorithms to particular settings, improving even more their accuracy. The paper also presents a fully automated pipeline for VPR database creation from RGBD scanning sequences, as well as a set of metrics for VPR database evaluation. The code and released data are available on our web-page~ -- https://prime-slam.github.io/place-recognition-db/

翻译：本文提出了一种方法，用于从RGBD扫描序列中创建适用于室内环境定位的视觉地点识别（VPR）数据库。该方法被形式化为一个基于图支配集算法的最小化问题，其中图由空间信息构建，并命名为DominatingSet。与其他数据库创建方法相比，我们的算法在场景覆盖方面表现更优。此外，我们证明使用DominatingSet时，数据库大小可缩减至原始扫描序列的1/250至1/1400，同时在测试序列上仍能保持超过80%的召回率。我们在7-scenes和BundleFusion数据集上，以及一个在高度重复的办公室环境中额外记录的序列上评估了算法。此外，该数据库选择方法能够生成弱监督标签，用于针对特定场景微调神经地点识别算法，从而进一步提升其准确性。本文还提出了一个从RGBD扫描序列中自动化创建VPR数据库的完整流程，以及一套用于评估VPR数据库的指标。代码和发布的数据可在我们的网页上获取：https://prime-slam.github.io/place-recognition-db/