An optimization-based IMU/Lidar/Camera Co-calibration method

TL;DR

This paper introduces an optimization-based method for jointly calibrating IMU, Lidar, and Camera sensors, improving efficiency and accuracy over traditional pairwise calibration approaches in multi-sensor fusion for autonomous navigation.

Contribution

The paper presents a unified optimization approach for IMU/Lidar/Camera co-calibration that refines all extrinsic parameters simultaneously, outperforming existing two-step methods.

Findings

Outperforms traditional two-step calibration methods in accuracy.

Demonstrates effectiveness in simulation environments.

Provides a more efficient and integrated calibration process.

Abstract

Recently, multi-sensors fusion has achieved significant progress in the field of automobility to improve navigation and position performance. As the prerequisite of the fusion algorithm, the demand for the extrinsic calibration of multi-sensors is growing. To calculate the extrinsic parameter, many researches have been dedicated to the two-step method, which integrates the respective calibration in pairs. It is inefficient and incompact because of losing sight of the constrain of all sensors. With regard to remove this burden, an optimization-based IMU/Lidar/Camera co-calibration method is proposed in the paper. Firstly, the IMU/camera and IMU/lidar online calibrations are conducted, respectively. Then, the corner and surface feature points in the chessboard are associated with the coarse result and the camera/lidar constraint is constructed. Finally, construct the co-calibration optimization to refine all extrinsic parameters. We evaluate the performance of the proposed scheme in simulation and the result demonstrates that our proposed method outperforms the two-step method.