Dominating Set Database Selection for Visual Place Recognition

Anastasiia Kornilova^1, Ivan Moskalenko^1,2, Timofei Pushkin^1,2, Fakhriddin Tojiboev¹, Rahim Tariverdizadeh¹, Gonzalo Ferrer¹

¹ Skolkovo Institute of Science and Technology (Skoltech), Center for AI Technology (CAIT)
² Software Engineering Department, Saint Petersburg State University
^* Indicates equal contribution

Paper Code

Abstract

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.

Library

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 is available on our GitHub repo.

Sk campus dataset

We have recorded two sequences in the Skoltech campus using Azure Kinect DK sensor. This environment contains repeated structures with similar design of desks, walls and doors and therefore provides more challenging conditions for a VPR algorithm in the indoor environment. You can download this dataset here.

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