GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors

TL;DR

GLIM is a GPU-accelerated 3D localization and mapping framework that combines range-inertial data, visual features, and global optimization to achieve real-time performance despite complex computations.

Contribution

The paper introduces a novel GPU-accelerated 3D localization and mapping system integrating range-inertial data, visual constraints, and efficient global optimization for improved accuracy and real-time operation.

Findings

Real-time performance achieved with GPU acceleration.

Effective handling of degenerated range data for several seconds.

Enhanced accuracy through multi-modal sensor fusion.

Abstract

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.