Rotation Initialization and Stepwise Refinement for Universal LiDAR Calibration
CoRR(2024)
Abstract
Autonomous systems often employ multiple LiDARs to leverage the integrated
advantages, enhancing perception and robustness. The most critical prerequisite
under this setting is the estimating the extrinsic between each LiDAR, i.e.,
calibration. Despite the exciting progress in multi-LiDAR calibration efforts,
a universal, sensor-agnostic calibration method remains elusive. According to
the coarse-to-fine framework, we first design a spherical descriptor TERRA for
3-DoF rotation initialization with no prior knowledge. To further optimize, we
present JEEP for the joint estimation of extrinsic and pose, integrating
geometric and motion information to overcome factors affecting the point cloud
registration. Finally, the LiDAR poses optimized by the hierarchical
optimization module are input to time syn- chronization module to produce the
ultimate calibration results, including the time offset. To verify the
effectiveness, we conduct extensive experiments on eight datasets, where 16
diverse types of LiDARs in total and dozens of calibration tasks are tested. In
the challenging tasks, the calibration errors can still be controlled within
5cm and 1 with a high success rate.
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