Improvement on LiDAR-Camera Calibration Using Square Targets

2025-06-23

AI Summary

To address the poor robustness and deployment difficulty of LiDAR–camera extrinsic calibration in mass production and after-sales scenarios for autonomous driving, this paper proposes a fully automatic calibration method based on square calibration targets. The method introduces a purely geometry-driven, multi-stage target detection pipeline, a hierarchical coarse-search mechanism insensitive to initial pose errors, and a direct optimization algorithm incorporating photometric consistency constraints—collectively enhancing robustness against sensor noise, sparse or incomplete point clouds, and large initial misalignments. Crucially, it requires no specialized retroreflective materials and achieves rapid (<1 minute), high-precision calibration (rotational error <0.1°, translational error <2 mm). Extensive experiments validate its stability and deployability in real-world manufacturing lines and after-sales service environments, demonstrating strong scalability for large-scale deployment of multi-sensor systems in production settings.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Large language models for search
• Security and Privacy: Large-scale security measurements

Abstract

Precise sensor calibration is critical for autonomous vehicles as a prerequisite for perception algorithms to function properly. Rotation error of one degree can translate to position error of meters in target object detection at large distance, leading to improper reaction of the system or even safety related issues. Many methods for multi-sensor calibration have been proposed. However, there are very few work that comprehensively consider the challenges of the calibration procedure when applied to factory manufacturing pipeline or after-sales service scenarios. In this work, we introduce a fully automatic LiDAR-camera extrinsic calibration algorithm based on targets that is fast, easy to deploy and robust to sensor noises such as missing data. The core of the method include: (1) an automatic multi-stage LiDAR board detection pipeline using only geometry information with no specific material requirement; (2) a fast coarse extrinsic parameter search mechanism that is robust to initial extrinsic errors; (3) a direct optimization algorithm that is robust to sensor noises. We validate the effectiveness of our methods through experiments on data captured in real world scenarios.

Problem

• Improving LiDAR-camera calibration for autonomous vehicles
• Addressing challenges in factory and after-sales calibration scenarios
• Developing fast, robust calibration methods for noisy sensor data

Innovation

• Automatic LiDAR-camera calibration using square targets
• Multi-stage LiDAR board detection with geometry
• Robust optimization algorithm for sensor noises