Object Detection for Indoor Localization System

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Braun, J (Author) Other; Mendes, J (Author) Other; Pereira, AI (Author) Other; Lima, J (Author) Other; Paulo Gomes da Costa (Author) FEUP

Conference proceedings International

Title: OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, OL2A 2022

Pages: 788-803

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

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

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16