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SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization
In this work, we propose a novel LiDAR localization framework, SGLoc, which decouples the pose estimation to point cloud correspondence regression and pose estimation via this correspondence.
LiDAR-based localization using universal encoding and memory-aware regression
However, current methods suffer from being retrained with specific source data whenever the scene changes, resulting in expensive computational costs, data privacy disclosure, and unreliable localization caused by the inability to memorize all data.
Review: deep learning on 3D point clouds
Deep learning is now the most powerful tool for data processing in computer vision, becoming the most preferred technique for tasks such as classification, segmentation, and detection.
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