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

2025-07-11

AI Summary

Extrinsic calibration of multi-LiDAR and GNSS-aided inertial navigation systems (GINS) in intelligent mining faces severe challenges—namely, the absence of calibration targets, non-overlapping fields of view, lack of accurate ground-truth trajectories, and observability degradation induced by vehicle planar motion. Method: This paper proposes a targetless extrinsic calibration method based on joint geometric–kinematic optimization. It innovatively incorporates GNSS mounting height constraints to recover unobservable parameters under planar motion, and establishes a multi-constraint optimization framework integrating point-cloud geometric registration, sensor motion consistency, and structural mounting priors. Contribution/Results: The method supports heterogeneous LiDAR configurations (mechanical and solid-state). Validated on both simulated and real-world mining datasets, it achieves calibration accuracy better than 5 cm and 0.1°, demonstrating robustness and precision sufficient for accurate global-frame alignment of multi-modal perception data under complex operational conditions.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• Calibrating multiple LiDARs and GNSS-INS without targets or overlapping views
• Addressing observability issues in planar motion for mining vehicles
• Enabling accurate sensor fusion for vehicle-road collaboration

Innovation

• Targetless extrinsic calibration for multiple LiDARs
• Joint optimization framework for parameter refinement
• Height-based observation model for planar motion