Real-Time Global Registration for Globally Consistent RGB-D SLAM

Real-time globally consistent camera localization is critical for visual simultaneous localization and mapping (SLAM) applications. Regardless the popularity of high efficient pose graph optimization as a backend in SLAM, its deficiency in accuracy can hardly benefit the reconstruction application. An alternative solution for the sake of high accuracy would be global registration, which minimizes the alignment error of all the corresponding observations, yet suffers from high complexity due to the tremendous observations that need to be considered. In this paper, we start by analyzing the complexity bottleneck of global point cloud registration problem, i.e., each observation (three-dimensional point feature) has to be linearized based on its local coordinate (camera poses), which however is nonlinear and dynamically changing, resulting in extensive computation during optimization. We further prove that such nonlinearity can be decoupled into linear component (feature position) and nonlinear components (camera poses), where the former linear one can be effectively represented by its compact second-order statistics, while the latter nonlinear one merely requires six degrees of freedom for each camera pose. Benefiting from the decoupled representation, the complexity can be significantly reduced without sacrifice in accuracy. Experiments show that the proposed algorithm achieves globally consistent pose estimation in real-time via CPU computing, and owns comparable accuracy as state-of-the-art that use GPU computing, enabling the practical usage of globally consistent RGB-D SLAM on highly computationally constrained devices.