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Transfusion: A Novel SLAM Method Focused on Transparent Objects
Recently RGB-D sensors have become very popular in the area of Simultaneous Localisation and Mapping (SLAM). The RGB-D SLAM approach relies heavily on the accuracy of the input depth map. However, refraction and reflection of transparent objects will result in false depth input of RGB-D cameras, which makes the traditional RGB-D SLAM algorithm unable to work correctly in the presence of transparent objects. In this paper, we propose a novel SLAM approach called transfusion that allows transparent object existence and recovery in the video input. Our method is composed of two parts. Transparent Objects Cut Iterative Closest Points (TC-ICP)is first used to recover camera pose, detecting and removing transparent objects from input to reduce the trajectory errors. Then Transparent Objects Reconstruction (TO-Reconstruction) is used to reconstruct the transparent objects and opaque objects separately. The opaque objects are reconstructed with the traditional method, and the transparent objects are reconstructed with the visual hull-based method. To evaluate our algorithm, we construct a new RGB-D SLAM database containing 25 video sequences. Each sequence has at least one transparent object. Experiments show that our approach can work adequately in scenes contain transparent objects while the existing approach can not handle them. Our approach significantly improves the accuracy of the camera trajectory and the quality of environment reconstruction.