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Open AccessArticle

Spherical-Model-Based SLAM on Full-View Images for Indoor Environments

by 1, 2 and 1,2,*
School of Electronic and Information Engineering, also with the Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China
Graduate School of Information Sciences, Hiroshima City University, Hiroshima 7313194, Japan
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2268;
Received: 14 October 2018 / Revised: 7 November 2018 / Accepted: 14 November 2018 / Published: 16 November 2018
As we know, SLAM (Simultaneous Localization and Mapping) relies on surroundings. A full-view image provides more benefits to SLAM than a limited-view image. In this paper, we present a spherical-model-based SLAM on full-view images for indoor environments. Unlike traditional limited-view images, the full-view image has its own specific imaging principle (which is nonlinear), and is accompanied by distortions. Thus, specific techniques are needed for processing a full-view image. In the proposed method, we first use a spherical model to express the full-view image. Then, the algorithms are implemented based on the spherical model, including feature points extraction, feature points matching, 2D-3D connection, and projection and back-projection of scene points. Thanks to the full field of view, the experiments show that the proposed method effectively handles sparse-feature or partially non-feature environments, and also achieves high accuracy in localization and mapping. An experiment is conducted to prove that the accuracy is affected by the view field. View Full-Text
Keywords: full-view image; spherical model; SLAM full-view image; spherical model; SLAM
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Li, J.; Wang, X.; Li, S. Spherical-Model-Based SLAM on Full-View Images for Indoor Environments. Appl. Sci. 2018, 8, 2268.

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