A Target-based Multi-LiDAR Multi-Camera Extrinsic Calibration System
This addresses the calibration challenge for autonomous vehicles, but it appears incremental as it builds on existing target-based methods with tailored optimizations.
The paper tackled the problem of extrinsic calibration for multi-LiDAR and multi-camera sensor suites in autonomous driving, proposing a target-based system using a custom ChArUco board and nonlinear optimization, and demonstrated its effectiveness with real-world warehouse data.
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.