Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric Occupancy Mapping
<|reference_start|>Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric Occupancy Mapping: 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.<|reference_end|>
@article{boche2024tightly-coupled,
    title={Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric
  Occupancy Mapping},
    author={Simon Boche, Sebasti'an Barbas Laina, Stefan Leutenegger},
    journal={arXiv preprint arXiv:2403.02280},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02280},
    primaryClass={cs.RO}
}