A Target-based Multi-LiDAR Multi-Camera Extrinsic Calibration System

TL;DR

This paper introduces a target-based calibration system for multi-LiDAR and multi-camera setups, improving alignment accuracy crucial for autonomous driving perception systems, using a custom calibration target and nonlinear optimization tested in real-world scenarios.

Contribution

It presents a novel calibration pipeline that enables cross-calibration of diverse sensors with limited prior knowledge, tailored for autonomous vehicle sensor suites.

Findings

Effective calibration demonstrated with real-world warehouse data

High accuracy in sensor alignment achieved

Feasibility of a unified calibration pipeline for various sensors

Abstract

Extrinsic Calibration represents the cornerstone of autonomous driving. Its accuracy plays a crucial role in the perception pipeline, as any errors can have implications for the safety of the vehicle. Modern sensor systems collect different types of data from the environment, making it harder to align the data. To this end, we propose a target-based extrinsic calibration system tailored for a multi-LiDAR and multi-camera sensor suite. This system enables cross-calibration between LiDARs and cameras with limited prior knowledge using a custom ChArUco board and a tailored nonlinear optimization method. We test the system with real-world data gathered in a warehouse. Results demonstrated the effectiveness of the proposed method, highlighting the feasibility of a unique pipeline tailored for various types of sensors.