Evaluation of Bags of Binary Words for Place Recognition in Challenging Scenarios

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Gaspar, AR (Autor) Outra; Nunes, A (Autor) Outra; Aníbal Castilho Coimbra de Matos (Autor) FEUP

Ata de Conferência Internacional

Título: 2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Páginas: 19-24

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00V-1W2

DOI: 10.1109/icarsc52212.2021.9429799

Abstract (EN): To perform autonomous tasks, robots in real-world environments must be able to navigate in dynamic and unknown spaces. To do so, they must recognize previously seen places to compensate for accumulated positional deviations. This task requires effective identification of recovered landmarks to produce a consistent map, and the use of binary descriptors is increasing, especially because of their compact representation. The visual Bag-of-Words (BoW) algorithm is one of the most commonly used techniques to perform appearance-based loop closure detection quickly and robustly. Therefore, this paper presents a behavioral evaluation of a conventional BoW scheme based on Oriented FAST and Rotated BRIEF (ORB) features for image similarity detection in challenging scenarios. For each scenario, full-indexing vocabularies are created to model the operating environment and evaluate the performance for recognizing previously seen places similar to online approaches. Experiments were conducted on multiple public datasets containing scene changes, perceptual aliasing conditions, or dynamic elements. The Bag of Binary Words technique shows a good balance to deal with such severe conditions at a low computational cost.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6