Automatic and Robust Camera-LiDAR Calibration Without Dedicated Targets

Core Concepts

A novel method for automatic and target-less camera-LiDAR calibration that leverages sensor motion estimates and deep learning-based 2D-to-3D point correspondences.

Abstract

The paper proposes MDPCalib, a novel method for automatic and target-less camera-LiDAR calibration. The approach comprises two steps: Coarse Registration: Utilizes visual and LiDAR odometry to obtain sensor motion estimates. Aligns the motion of both sensors to initialize the extrinsic calibration parameters. Fine Registration: Employs a deep learning-based method (CMRNext) to find 2D pixel to 3D point correspondences. Jointly optimizes the calibration parameters with respect to both sensor motion and point correspondences, leveraging their complementary information. The authors demonstrate the effectiveness of MDPCalib on various robotic platforms, including the public KITTI dataset and three in-house configurations. Compared to previous methods, MDPCalib achieves significantly more accurate calibration results without requiring any human supervision or dedicated calibration targets. The authors also provide an extensive analysis of the impact of different design choices and parameters on the calibration performance.

Stats

The translation error magnitude is reduced from 39.37 cm to 2.94 cm, and the rotation error magnitude is reduced from 0.51 degrees to 0.14 degrees through the proposed two-stage optimization.

Quotes

"We introduce MDPCalib for automatic target-less camera-LiDAR calibration that requires neither human initialization nor special data recording." "We propose to formulate extrinsic calibration as a graph optimization problem constrained by sensor motion and deep learning-based point correspondences."

Key Insights Distilled From

Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences

by Kürs... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17298.pdf

Deeper Inquiries

How could MDPCalib be extended to handle multiple cameras and LiDARs simultaneously?

MDPCalib could be extended to handle multiple cameras and LiDARs simultaneously by modifying the optimization framework to incorporate the calibration parameters for each sensor pair. This would involve extending the graph optimization problem to include constraints for each additional camera-LiDAR pair. The calibration process would need to account for the extrinsic relationships between each camera and LiDAR sensor, as well as the relationships between multiple cameras and multiple LiDARs. By including these additional constraints in the optimization problem, MDPCalib could simultaneously calibrate multiple sensor setups, enabling robust sensor fusion across a variety of configurations.

What are the potential challenges in incorporating intrinsic calibration into the optimization framework of MDPCalib?

Incorporating intrinsic calibration into the optimization framework of MDPCalib could pose several challenges. One challenge is the complexity of the optimization problem, as intrinsic calibration parameters introduce additional variables that need to be optimized simultaneously with the extrinsic parameters. This could increase the computational complexity and the risk of local minima during optimization. Additionally, intrinsic calibration parameters are often highly sensitive and can have a significant impact on the overall calibration accuracy. Ensuring the robustness and stability of the optimization process while incorporating intrinsic calibration could be challenging. Moreover, the calibration process may require additional calibration patterns or procedures to accurately estimate the intrinsic parameters, adding complexity to the calibration pipeline.

Could MDPCalib be adapted for online calibration to handle dynamic changes in the sensor setup over time?

MDPCalib could be adapted for online calibration to handle dynamic changes in the sensor setup over time by implementing a continuous calibration process that updates the calibration parameters in real-time as the sensor setup changes. This adaptation would involve integrating a mechanism for continuously collecting sensor data, estimating sensor motion, and updating the calibration parameters based on the new data. By incorporating online calibration capabilities, MDPCalib could adapt to changes in the sensor setup, such as sensor repositioning or replacement, ensuring that the calibration remains accurate and up-to-date. However, challenges may arise in maintaining the stability and accuracy of the calibration process in real-time, as well as in efficiently updating the calibration parameters without disrupting the system's operation.