Glass confidence maps building based on neural networks using laser range-finders for mobile robots

Jun Jiang, Renato Miyagusuku, Atsushi Yamashita, Hajime Asama

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19 Citas (Scopus)

Resumen

In this paper, we propose a method to classify glass and non-glass objects and build glass confidence maps for indoor mobile robots using laser range-finders (LRFs). The glass confidence map is aimed to improve robot localization systems' robustness and accuracy in glass environments. For most LRF-based localization systems, objects are assumed to be detectable from all incident angles, which is true for non-reflective and non-Transparent objects, like walls. However, glass can only be detected by LRFs in certain incident angles. This glass detection failure decreases robots' localization accuracy. Exhibiting glass' position in the map and taking its detection failure into consideration can increase the localization accuracy. We propose the usage of a neural network to classify glass and non-glass objects, with LRF's measured intensity, distance and incident angles as inputs. We verified our method experimentally, and experimental results show that our method can successfully distinguish glass from non-glass objects and accurately construct a glass confidence map with high confidence.

Idioma originalInglés
Título de la publicación alojadaSII 2017 - 2017 IEEE/SICE International Symposium on System Integration
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas405-410
Número de páginas6
ISBN (versión digital)9781538622636
DOI
EstadoPublicada - 2 jul. 2017
Publicado de forma externa
Evento2017 IEEE/SICE International Symposium on System Integration, SII 2017 - Taipei, Taiwán
Duración: 11 dic. 201714 dic. 2017

Serie de la publicación

NombreSII 2017 - 2017 IEEE/SICE International Symposium on System Integration
Volumen2018-January

Conferencia

Conferencia2017 IEEE/SICE International Symposium on System Integration, SII 2017
País/TerritorioTaiwán
CiudadTaipei
Período11/12/1714/12/17

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