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.
Lidar-based local- ization using universal encoding and memory-aware regres- sion.PR, 128:108685
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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.