A registration and planning system uses flawed prior maps to guide LiDAR UAVs, reporting up to 34.2% better efficiency and 37.9% less flight distance than prior methods.
Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map
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A 4D radar-only occupancy model trained with LiDAR pseudo-labels and height-map supervision approaches LiDAR-quality occupancy and runs at about 30 Hz.
A hierarchical offline-online framework for 3D global relocalization using synthetic LiDAR and descriptor retrieval achieves 3-second average time and 8 cm accuracy with order-of-magnitude efficiency gains over prior methods.
FP16 quantization preserves accuracy in BEV-based LiDAR place recognition at lower cost while INT8 degradation depends on the network architecture.
citing papers explorer
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Explore From Sketch: Accelerating UAV Exploration in Large-scale Environments with Prior Maps
A registration and planning system uses flawed prior maps to guide LiDAR UAVs, reporting up to 34.2% better efficiency and 37.9% less flight distance than prior methods.
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4D-ROLLS: 4D Radar Occupancy Learning via LiDAR Supervision
A 4D radar-only occupancy model trained with LiDAR pseudo-labels and height-map supervision approaches LiDAR-quality occupancy and runs at about 30 Hz.
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Offline-Online Hierarchical 3D Global Relocalization With Synthetic LiDAR Sensing and Descriptor-Space Retrieval
A hierarchical offline-online framework for 3D global relocalization using synthetic LiDAR and descriptor retrieval achieves 3-second average time and 8 cm accuracy with order-of-magnitude efficiency gains over prior methods.
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EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition
FP16 quantization preserves accuracy in BEV-based LiDAR place recognition at lower cost while INT8 degradation depends on the network architecture.