3D SLAM for scenes with repetitive texture inside Tokamak chamber

Abstract

As the intra-scene of Tokamak chamber contains many repetitive textures, the traditional 3D reconstruction method based on the feature descriptor would be difficult to work well since the image matching algorithms based on feature descriptor are unstable and may fail sometimes in this environment. To address this problem, a novel multilevel matching algorithm is proposed, which uses the structural characteristics of Tokamak chamber as prior knowledge to find reliable correspondence points between two images. Firstly, each image is divided into basic structure regions. Then, to obtain the corresponding relation of structure regions from multiple images, we take the preliminary matching on the structural framework. The feature points is matched inner the structure regions to ensure the correctness of the feature matching. To testify the effectiveness of the proposed algorithm, it is applied to repetitive texture images captured in the Tokamak chamber, and the experimental results show that more correct matching points are acquired, smooth and clear 3D point-cloud data are generated, and high accurate and integrated reconstruct results are obtained.