RT Journal Article
T1 Semantic 3D mapping from deep image segmentation
A1 Martin, Francisco
A1 Gonzalez, Fernando
A1 Guerrero, Jose Miguel
A1 Fernandez-Carmona, Manuel
A1 Gines, Jonatan
K1 Ciencias aplicadas
K1 Robots autónomos
AB 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.
PB MDPI
YR 2021
FD 2021
LK https://hdl.handle.net/10630/34174
UL https://hdl.handle.net/10630/34174
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026