Planar LiDAR Densified Simulation from Sparse Visual SLAM

In order to address the shortcomings of feature-based sparse VSLAM (visual simultaneous localization and mapping) in visual navigation because of its sparse mapping, a densifying algorithm based on Gaussian filter interpolation is proposed to simulate the planar LiDAR feedback. A densifying algorithm based on Gaussian distribution and circulated filtering is proposed. By establishing global Gaussian filter and local Gaussian distribution estimation, the planar projection of the sparse VSLAM spatial points is interpolated to generate a simulated planar LiDAR feedback. With CPU only, the proposed algorithm costs 0.0003s~0.006s for each frame, and gets a 7.956% relative error in its interpolation results. The experiment results demonstrate that the proposed interpolation method can densify the sparse projected points, the result is similar to the true LiDAR feedback, and it provides an effective real-time front-end sensor processing method for visual navigation.