Semantic 3D Mapping from Deep Image Segmentation

• Main Authors: Francisco Martín, Fernando González, José Miguel Guerrero, Manuel Fernández, Jonatan Ginés
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Applied Sciences
• Subjects: image segmentation; deep learning; 3D semantic mapping
• Online Access: https://www.mdpi.com/2076-3417/11/4/1953

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.