Target-free Extrinsic Calibration of a 3D-Lidar and an IMU

TL;DR

This paper introduces a target-free calibration method for aligning a 3D Lidar and an IMU using an EKF that leverages motion constraints, validated through lab experiments and open-source code.

Contribution

A novel target-free calibration algorithm for 3D Lidar and IMU using EKF and motion constraints, with experimental validation and open-source implementation.

Findings

Accurate inter-sensor rotation determined via least squares optimization.

Effective translation estimation using motion-based constraints within EKF.

Validated method demonstrates reliable calibration without calibration targets.

Abstract

This work presents a novel target-free extrinsic calibration algorithm for a 3D Lidar and an IMU pair using an Extended Kalman Filter (EKF) which exploits the \textit{motion based calibration constraint} for state update. The steps include, data collection by motion excitation of the Lidar Inertial Sensor suite along all degrees of freedom, determination of the inter sensor rotation by using rotational component of the aforementioned \textit{motion based calibration constraint} in a least squares optimization framework, and finally, the determination of inter sensor translation using the \textit{motion based calibration constraint} for state update in an Extended Kalman Filter (EKF) framework. We experimentally validate our method using data collected in our lab and open-source (https://github.com/unmannedlab/imu_lidar_calibration) our contribution for the robotics research community.