Improvement on LiDAR-Camera Calibration Using Square Targets

TL;DR

This paper presents a fully automatic, robust, and fast LiDAR-camera calibration method using square targets, suitable for factory and after-sales scenarios, improving calibration accuracy and deployment efficiency.

Contribution

The authors introduce a novel automatic calibration algorithm that is fast, noise-robust, and easy to deploy, addressing challenges in manufacturing and service environments.

Findings

Effective in real-world scenarios

Robust to sensor noise and missing data

Fast and easy to deploy

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.