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Open AccessArticle

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

by 1, 2 and 1,2,*
1
School of Electronic and Information Engineering, also with the Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China
2
Graduate School of Information Sciences, Hiroshima City University, Hiroshima 7313194, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2268; https://doi.org/10.3390/app8112268
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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