In robotics, visual place recognition (VPR) is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This work proposes a taxonomy of the architectures used to learn sequential descriptors for VPR, highlighting different mechanisms to fuse the information from the individual images. This categorization is supported by a complete benchmark of experimental results that provides evidence of the strengths and weaknesses of these different architectural choices. The analysis is not limited to existing sequential descriptors, but we extend it further to investigate the viability of Transformers instead of CNN backbones. We further propose a new ad-hoc sequence-level aggregator called SeqVLAD, which outperforms prior state of the art on different datasets. The code is available at https://github.com/vandal-vpr/vg-transformers.

Learning Sequential Descriptors for Sequence-Based Visual Place Recognition / Mereu, R; Trivigno, G; Berton, G; Masone, C; Caputo, B. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 7:4(2022), pp. 10383-10390. [10.1109/LRA.2022.3194310]

Learning Sequential Descriptors for Sequence-Based Visual Place Recognition

Trivigno, G;Berton, G;Masone, C;Caputo, B
2022

Abstract

In robotics, visual place recognition (VPR) is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This work proposes a taxonomy of the architectures used to learn sequential descriptors for VPR, highlighting different mechanisms to fuse the information from the individual images. This categorization is supported by a complete benchmark of experimental results that provides evidence of the strengths and weaknesses of these different architectural choices. The analysis is not limited to existing sequential descriptors, but we extend it further to investigate the viability of Transformers instead of CNN backbones. We further propose a new ad-hoc sequence-level aggregator called SeqVLAD, which outperforms prior state of the art on different datasets. The code is available at https://github.com/vandal-vpr/vg-transformers.
File in questo prodotto:
File Dimensione Formato  
Learning_Sequential_Descriptors_for_Sequence-Based_Visual_Place_Recognition.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 2.15 MB
Formato Adobe PDF
2.15 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2971242