LEADER cuts position error by 24.1% on Oxford RobotCar and 73.9% on NCLT by combining a projection-based geometric encoder with a truncated relative reliability loss that down-weights unreliable points.
Quatro++: Robust global registration exploiting ground segmentation for loop closing in lidar slam.IJRR, 43(5):685–715
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LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalization
LEADER cuts position error by 24.1% on Oxford RobotCar and 73.9% on NCLT by combining a projection-based geometric encoder with a truncated relative reliability loss that down-weights unreliable points.