Semantic 3D mapping from deep image segmentation - Martin, Francisco; Gonzalez, Fernando; Guerrero, Jose Miguel; Fernandez-Carmona, Manuel; Gines, Jonatan (MDPI, 2021)
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: Ciencias aplicadas; Robots autónomos; Image segmentation; Deep learning; 3D semantic mapping