  Simultaneous Localization and Mapping (SLAM) is an essential component of
autonomous robotic applications and self-driving vehicles, enabling them to
understand and operate in their environment. Many SLAM systems have been
proposed in the last decade, but they are often complex to adapt to different
settings or sensor setups. In this work, we present LiDAR Graph-SLAM (LG-SLAM),
a versatile range-inertial SLAM framework that can be adapted to different
types of sensors and environments, from underground mines to offices with
minimal parameter tuning. Our system integrates range, inertial and GNSS
measurements into a graph-based optimization framework. We also use a refined
submap management approach and a robust loop closure method that effectively
accounts for uncertainty in the identification and validation of putative loop
closures, ensuring global consistency and robustness. Enabled by a parallelized
architecture and GPU integration, our system achieves pose estimation at LiDAR
frame rate, along with online loop closing and graph optimization. We validate
our system in diverse environments using public datasets and real-world data,
consistently achieving an average error below 20 cm and outperforming other
state-of-the-art algorithms.
