Object Detection for Indoor Localization System

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Braun, J (Autor) Outra; Mendes, J (Autor) Outra; Pereira, AI (Autor) Outra; Lima, J (Autor) Outra; Paulo Gomes da Costa (Autor) FEUP

Ata de Conferência Internacional

Título: OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, OL2A 2022

Páginas: 788-803

2nd International Conference on Optimization, Learning Algorithms and Applications (OL2A)

Povoa de Varzim, PORTUGAL, OCT 24-25, 2022

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: P-00X-PC1

DOI: 10.1007/978-3-031-23236-7_54

Abstract (EN): The urge for robust and reliable localization systems for autonomous mobile robots (AMR) is increasing since the demand for these automated systems is rising in service, industry, and other areas of the economy. The localization of AMRs is one of the crucial challenges, and several approaches exist to solve this. The most well-known localization systems are based on LiDAR data due to their reliability, accuracy, and robustness. One standard method is to match the reference map information with the actual readings from LiDAR or camera sensors, allowing localization to be performed. However, this approach has difficulties handling anything that does not belong to the original map since it affects the matching algorithm's performance. Therefore, they should be considered outliers. In this paper, a deep learning-based object detection algorithm is not only used for detection but also to classify them as outliers from the localization's perspective. This is an innovative approach to improve the localization results in a realmobile platform. Results are encouraging, and the proposed methodology is being tested in a real robot.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16