  This article presents GLIM, a 3D range-inertial localization and mapping
framework with GPU-accelerated scan matching factors. The odometry estimation
module of GLIM employs a combination of fixed-lag smoothing and keyframe-based
point cloud matching that makes it possible to deal with a few seconds of
completely degenerated range data while efficiently reducing trajectory
estimation drift. It also incorporates multi-camera visual feature constraints
in a tightly coupled way to further improve the stability and accuracy. The
global trajectory optimization module directly minimizes the registration
errors between submaps over the entire map. This approach enables us to
accurately constrain the relative pose between submaps with a small overlap.
Although both the odometry estimation and global trajectory optimization
algorithms require much more computation than existing methods, we show that
they can be run in real-time due to the careful design of the registration
error evaluation algorithm and the entire system to fully leverage GPU parallel
processing.
