Sylvester-form-based resultant solvers for pose estimation run faster than existing closed-form methods while matching their numerical accuracy on 3D-3D and 3D-2D problems.
Vision meets robotics: The KITTI dataset
2 Pith papers cite this work. Polarity classification is still indexing.
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Multi-FEAT performs targetless camera-LiDAR extrinsic calibration by encoding LiDAR data as cylindrical panoramas, supplementing sparse boundaries with diverse features, extracting camera edges via segmentation, and optimizing via a feature-matching function, with better reliability than prior arton
citing papers explorer
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Efficient closed-form approaches for pose estimation using Sylvester forms
Sylvester-form-based resultant solvers for pose estimation run faster than existing closed-form methods while matching their numerical accuracy on 3D-3D and 3D-2D problems.
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Multi-FEAT: Multi-Feature Edge Alignment for Targetless Camera-LiDAR Calibration
Multi-FEAT performs targetless camera-LiDAR extrinsic calibration by encoding LiDAR data as cylindrical panoramas, supplementing sparse boundaries with diverse features, extracting camera edges via segmentation, and optimizing via a feature-matching function, with better reliability than prior arton