  Autonomous navigation is one of the key requirements for every potential
application of mobile robots in the real-world. Besides high-accuracy state
estimation, a suitable and globally consistent representation of the 3D
environment is indispensable. We present a fully tightly-coupled
LiDAR-Visual-Inertial SLAM system and 3D mapping framework applying local
submapping strategies to achieve scalability to large-scale environments. A
novel and correspondence-free, inherently probabilistic, formulation of LiDAR
residuals is introduced, expressed only in terms of the occupancy fields and
its respective gradients. These residuals can be added to a factor graph
optimisation problem, either as frame-to-map factors for the live estimates or
as map-to-map factors aligning the submaps with respect to one another.
Experimental validation demonstrates that the approach achieves
state-of-the-art pose accuracy and furthermore produces globally consistent
volumetric occupancy submaps which can be directly used in downstream tasks
such as navigation or exploration.
