Visual and Inertial Data Fusion for Globally Consistent Point Cloud Registration

This work addresses the problem of aligning a set of point clouds acquired by an RGB-D sensor. To achieve this, we propose the fusion of RGB-D data with the orientation provided by a MARG (Magnetic, Angular Rate and Gravity) sensor, a combination that hasn't been extensively explored in the literature. Our methodology uses MARG data both in the coarse pair wise alignment between point clouds and in the loop closure detection between key frames. In our experiments, we were able to align the walls of a room with dimensions 9.84m x 7.13m. Our analysis shows us that MARG data helps to improve the alignment quality.