SLD-MAP: Surfel-Line Real-time Dense Mapping

We propose a dense mapping algorithm based on surfel with line constraint, called SLD-MAP for room-scale and urban-size environment, which aims to improve reconstruction accuracy and reduce void space on the reconstruction surface. We apply visual odometry to estimate camera poses, and reconstruct the 3D environment according to the input depth image and RGB image. The first step is to optimize the pose with line constraints. The second step is to extract the superpixel and resize the radius and position of the superpixel with line constraints. The third step is to generate surfels and fuse them with local maps. The fourth step is plane fitting of local map. The last step is to update the local map and deform the global map. Finally, the reconstruction accuracy is evaluated on public datasets, compare with the state-of-the-art methods.