Adaptive LiDAR–Inertial–GNSS SLAM Framework for Georeferenced Mapping in Challenging Urban Environments

Maintaining accurate, georeferenced dense maps requires reliable global navigation satellite systems (GNSS) observations to constrain global drift. Degraded observations must be prevented from distorting trajectories and local maps as reception transitions from open sky to urban obstruction and complete signal denial indoors. This study develops and evaluates Dense LiDAR–Inertial–GNSS simultaneous localization and mapping (DLIG-SLAM) to adapt GNSS integration and local mapping as reception conditions change. Built on Dense Multi-Scan Adjustment, the framework estimates the local trajectory and dense map separately from the transformation that connects them to a global coordinate frame. GNSS observations are assessed separately for updating this transformation and correcting the estimator. An online switcher selects among integration strategies according to measurement reliability, estimator readiness, and trajectory consistency. Outdoor–indoor state changes reconfigure local mapping, while voxel size adapts to the observed scan point population. Evaluation was performed on four KITTI driving sequences and an urban canyon outdoor–indoor mission in Toronto, recorded with standalone single-point positioning GNSS. GNSS coupling through an inertial navigation estimate achieved the lowest three-dimensional trajectory root mean square error on three of the four KITTI sequences. Online switching did not consistently outperform fixed integration strategies in the KITTI trajectory evaluation. In the Toronto dataset, inertial navigation-mediated coupling achieved the lowest horizontal georeferencing root mean square error of 1.29 m, while full-navigation factor graph smoothing achieved the lowest three-dimensional georeferencing root mean square error (RMSE) of 1.71 m. Online switching achieved the lowest cloud-to-cloud root mean square distance of 6.1 cm against a terrestrial laser scanning reference within the evaluated facade and ground floor region. Controlled experiments also evaluated the effect of separate GNSS admission paths and global transformation policies. The results support adapting GNSS integration to changing reception conditions and mapping objectives, as stronger coupling did not uniformly improve trajectory accuracy, global placement, and local geometric agreement.