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Hybrid LiDAR-inertial-visual odometry (LIVO) robustly handles visually challenging conditions, outperforming sparse-direct methods by combining direct photometric methods with learning-based feature descriptors.
GenZ-LIO tackles the LiDAR-inertial odometry challenge of spatial scale variation, enabling robust and efficient localization across indoor and outdoor environments.
