CT-VoxelMap achieves more accurate and efficient continuous-time LiDAR-inertial odometry by estimating control point increments on Lie groups, using IMU data to correct B-spline fitting errors online, and managing a probabilistic adaptive voxel map with a re-estimation policy.
On calculating with b-splines
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B-spline curve fitting unwraps 3D fingerprint point clouds into 2D grayscale images, achieving EERs of 0.2072%, 0.26%, and 0.22% and outperforming prior 3D methods in cross-session tests.
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CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping
CT-VoxelMap achieves more accurate and efficient continuous-time LiDAR-inertial odometry by estimating control point increments on Lie groups, using IMU data to correct B-spline fitting errors online, and managing a probabilistic adaptive voxel map with a re-estimation policy.
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A B-Spline Function Based 3D Point Cloud Unwrapping Scheme for 3D Fingerprint Recognition and Identification
B-spline curve fitting unwraps 3D fingerprint point clouds into 2D grayscale images, achieving EERs of 0.2072%, 0.26%, and 0.22% and outperforming prior 3D methods in cross-session tests.