Robot Semantic Localization Through CNN Descriptors

 

Authors

Cruz, Edmanuel; Rangel, José Carlos; Cazorla, Miguel

Format

Article

Status

publishedVersion

Description

Semantic localization for mobile robots involves an accurate determination of the kind of place where a robot is located. Therefore, the representation of the knowledge of this place is crucial for the robot. In this paper we present a study for finding a robust model for scene classification procedure for a mobile robot. The proposed system uses CNN descriptors for representing the input perceptions of the robot. First, we develop comparative experiments in order for finding a model. Experiments include the evaluation of several pre-trained CNN models and training a classifier with different classifications algorithms. These experiments were carried out using the ViDRILO dataset and compared with the benchmark provided by their authors. The results demonstrate the goodness of using CNN descriptors for semantic classification.
Semantic localization for mobile robots involves an accurate determination of the kind of place where a robot is located. Therefore, the representation of the knowledge of this place is crucial for the robot. In this paper we present a study for finding a robust model for scene classification procedure for a mobile robot. The proposed system uses CNN descriptors for representing the input perceptions of the robot. First, we develop comparative experiments in order for finding a model. Experiments include the evaluation of several pre-trained CNN models and training a classifier with different classifications algorithms. These experiments were carried out using the ViDRILO dataset and compared with the benchmark provided by their authors. The results demonstrate the goodness of using CNN descriptors for semantic classification.

Publication Year

2020

Language

eng

Topic

Semantic localization
Deep Learning
Environment perception
Image processing
Semantic localization
Deep Learning
Environment perception
Image processing

Repository

RI de Documento Digitales de Acceso Abierto de la UTP

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https://link.springer.com/chapter/10.1007/978-3-319-70833-1_46
https://ridda2.utp.ac.pa/handle/123456789/9444

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