  Large-scale multi-session LiDAR mapping is essential for a wide range of
applications, including surveying, autonomous driving, crowdsourced mapping,
and multi-agent navigation. However, existing approaches often struggle with
data redundancy, robustness, and accuracy in complex environments. To address
these challenges, we present MS-Mapping, an novel multi-session LiDAR mapping
system that employs an incremental mapping scheme for robust and accurate map
assembly in large-scale environments. Our approach introduces three key
innovations: 1) A distribution-aware keyframe selection method that captures
the subtle contributions of each point cloud frame to the map by analyzing the
similarity of map distributions. This method effectively reduces data
redundancy and pose graph size, while enhancing graph optimization speed; 2) An
uncertainty model that automatically performs least-squares adjustments
according to the covariance matrix during graph optimization, improving mapping
precision, robustness, and flexibility without the need for scene-specific
parameter tuning. This uncertainty model enables our system to monitor pose
uncertainty and avoid ill-posed optimizations, thereby increasing adaptability
to diverse and challenging environments. 3) To ensure fair evaluation, we
redesign baseline comparisons and the evaluation benchmark. Direct assessment
of map accuracy demonstrates the superiority of the proposed MS-Mapping
algorithm compared to state-of-the-art methods. In addition to employing public
datasets such as Urban-Nav, FusionPortable, and Newer College, we conducted
extensive experiments on such a large \SI{855}{m}$\times$\SI{636}{m} ground
truth map, collecting over \SI{20}{km} of indoor and outdoor data across more
than ten sequences...
