Joint Optimization-based Targetless Extrinsic Calibration for Multiple LiDARs and GNSS-Aided INS of Ground Vehicles

TL;DR

This paper introduces a targetless, joint optimization method for calibrating multiple LiDAR sensors to a GNSS-aided INS in ground vehicles, overcoming limitations of existing approaches by not requiring targets or overlapping views.

Contribution

It proposes a novel calibration framework that leverages known installation height and joint optimization to calibrate heterogeneous LiDARs without external targets or overlapping fields of view.

Findings

Achieves high calibration accuracy in simulated and real-world tests.

Demonstrates robustness under diverse sensor configurations and planar motion.

Eliminates the need for artificial calibration targets.

Abstract

Accurate extrinsic calibration between multiple LiDAR sensors and a GNSS-aided inertial navigation system (GINS) is essential for achieving reliable sensor fusion in intelligent mining environments. Such calibration enables vehicle-road collaboration by aligning perception data from vehicle-mounted sensors to a unified global reference frame. However, existing methods often depend on artificial targets, overlapping fields of view, or precise trajectory estimation, which are assumptions that may not hold in practice. Moreover, the planar motion of mining vehicles leads to observability issues that degrade calibration performance. This paper presents a targetless extrinsic calibration method that aligns multiple onboard LiDAR sensors to the GINS coordinate system without requiring overlapping sensor views or external targets. The proposed approach introduces an observation model based on the known installation height of the GINS unit to constrain unobservable calibration parameters under planar motion. A joint optimization framework is developed to refine both the extrinsic parameters and GINS trajectory by integrating multiple constraints derived from geometric correspondences and motion consistency. The proposed method is applicable to heterogeneous LiDAR configurations, including both mechanical and solid-state sensors. Extensive experiments on simulated and real-world datasets demonstrate the accuracy, robustness, and practical applicability of the approach under diverse sensor setups.