Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization
<|reference_start|>Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization: This paper presents a range inertial localization algorithm for a 3D prior map. The proposed algorithm tightly couples scan-to-scan and scan-to-map point cloud registration factors along with IMU factors on a sliding window factor graph. The tight coupling of the scan-to-scan and scan-to-map registration factors enables a smooth fusion of sensor ego-motion estimation and map-based trajectory correction that results in robust tracking of the sensor pose under severe point cloud degeneration and defective regions in a map. We also propose an initial sensor state estimation algorithm that robustly estimates the gravity direction and IMU state and helps perform global localization in 3- or 4-DoF for system initialization without prior position information. Experimental results show that the proposed method outperforms existing state-of-the-art methods in extremely severe situations where the point cloud data becomes degenerate, there are momentary sensor interruptions, or the sensor moves along the map boundary or into unmapped regions.<|reference_end|>
@article{koide2024tightly,
    title={Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on
  Sliding Window Factor Graph Optimization},
    author={Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno},
    journal={arXiv preprint arXiv:2402.05540},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05540},
    primaryClass={cs.RO}
}