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

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

Blog Article

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 Basketball 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 Kids Shoes 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.