Semantic 3D Mapping from Deep Image Segmentation

Martín, Francisco; González, Fernando; Guerrero, José Miguel; Fernández, Manuel; Ginés, Jonatan

doi:10.3390/app11041953

Open AccessArticle

Semantic 3D Mapping from Deep Image Segmentation

by

Francisco Martín

^1,*

,

Fernando González

¹,

José Miguel Guerrero

¹

,

Manuel Fernández

¹

and

Jonatan Ginés

²

¹

Intelligent Robotics Lab, Rey Juan Carlos University, 28943 Fuenlabrada, Spain

²

Escuela Internacional de Doctorado, Rey Juan Carlos University, 28933 Móstoles, Spain

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2021, 11(4), 1953; https://doi.org/10.3390/app11041953

Submission received: 28 January 2021 / Revised: 11 February 2021 / Accepted: 17 February 2021 / Published: 23 February 2021

(This article belongs to the Special Issue Deep Image Semantic Segmentation and Recognition)

Download

Browse Figures

Review Reports Versions Notes

Abstract

The perception and identification of visual stimuli from the environment is a fundamental capacity of autonomous mobile robots. Current deep learning techniques make it possible to identify and segment objects of interest in an image. This paper presents a novel algorithm to segment the object’s space from a deep segmentation of an image taken by a 3D camera. The proposed approach solves the boundary pixel problem that appears when a direct mapping from segmented pixels to their correspondence in the point cloud is used. We validate our approach by comparing baseline approaches using real images taken by a 3D camera, showing that our method outperforms their results in terms of accuracy and reliability. As an application of the proposed algorithm, we present a semantic mapping approach for a mobile robot’s indoor environments.

Keywords: image segmentation; deep learning; 3D semantic mapping

Share and Cite

MDPI and ACS Style

Martín, F.; González, F.; Guerrero, J.M.; Fernández, M.; Ginés, J. Semantic 3D Mapping from Deep Image Segmentation. Appl. Sci. 2021, 11, 1953. https://doi.org/10.3390/app11041953

AMA Style

Martín F, González F, Guerrero JM, Fernández M, Ginés J. Semantic 3D Mapping from Deep Image Segmentation. Applied Sciences. 2021; 11(4):1953. https://doi.org/10.3390/app11041953

Chicago/Turabian Style

Martín, Francisco, Fernando González, José Miguel Guerrero, Manuel Fernández, and Jonatan Ginés. 2021. "Semantic 3D Mapping from Deep Image Segmentation" Applied Sciences 11, no. 4: 1953. https://doi.org/10.3390/app11041953

APA Style

Martín, F., González, F., Guerrero, J. M., Fernández, M., & Ginés, J. (2021). Semantic 3D Mapping from Deep Image Segmentation. Applied Sciences, 11(4), 1953. https://doi.org/10.3390/app11041953

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Semantic 3D Mapping from Deep Image Segmentation

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI