li_slam_ros2

Leverages LIO-SAM's proven optimization pipeline for robust odometry, as demonstrated in the walking dataset with corrected paths.

Implements both NDT and GICP algorithms for flexible and precise point cloud alignment, enhancing adaptability to different environments.

Corrects drift by recognizing revisited locations and optimizing the pose graph, shown in the campus dataset with improved trajectory accuracy.

Provides ready-to-use launch files, RViz configurations, and service calls for map saving, enabling seamless deployment in ROS 2 ecosystems.