  In this paper, we propose a novel dense surfel mapping system that scales
well in different environments with only CPU computation. Using a sparse SLAM
system to estimate camera poses, the proposed mapping system can fuse intensity
images and depth images into a globally consistent model. The system is
carefully designed so that it can build from room-scale environments to
urban-scale environments using depth images from RGB-D cameras, stereo cameras
or even a monocular camera. First, superpixels extracted from both intensity
and depth images are used to model surfels in the system. superpixel-based
surfels make our method both run-time efficient and memory efficient. Second,
surfels are further organized according to the pose graph of the SLAM system to
achieve $O(1)$ fusion time regardless of the scale of reconstructed models.
Third, a fast map deformation using the optimized pose graph enables the map to
achieve global consistency in real-time. The proposed surfel mapping system is
compared with other state-of-the-art methods on synthetic datasets. The
performances of urban-scale and room-scale reconstruction are demonstrated
using the KITTI dataset and autonomous aggressive flights, respectively. The
code is available for the benefit of the community.

Robotics;Computer Vision and Pattern Recognition