Rotation Initialization and Stepwise Refinement for Universal LiDAR Calibration

TL;DR

This paper introduces a universal LiDAR calibration framework combining rotation initialization, joint extrinsic and pose estimation, and time synchronization, achieving high accuracy across diverse datasets and sensor types.

Contribution

It proposes a sensor-agnostic calibration method with a spherical descriptor for rotation initialization and a joint optimization approach for extrinsic and pose estimation.

Findings

Calibration errors within 5cm and 1° in challenging tasks

Effective across 16 different LiDAR types and multiple datasets

High success rate in diverse calibration scenarios

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 synchronization 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{\deg} with a high success rate.