Geometric calibration of a multi-layer LiDAR system and image sensors using plane-based implicit laser parameters for textured 3-D depth reconstruction

• Novel approach to for the optimization of the intrinsic and extrinsic parameters.
• A linear method to solve the translation and rotation parts of the transformation.
• Non-linear optimization reduces the dimension of the parameter space.
• Reduces RMS error by over 50% and running time of calibration by 87%.

The paper proposes the calibration of a LiDAR-camera system that consists of a multi-layer laser rangefinder device and a pair of video cameras. The method calibrates the intrinsic laser parameters and the extrinsic parameters of the integrated LiDAR-camera system. Using a linear form, the dimensionality of the calibration parameter space is reduced in the plane-based least square model. The optimal laser intrinsic parameters can be determined during the optimization of the extrinsic parameters, without being explicitly modeled. However, due to limited FOV of the cameras, the reduced model may lead to a solution that cannot be generalized to the working space. Hence, we use additional scene planes to improve the determination of intrinsic laser parameters. Overall performance is improved if calibration targets can be accurately estimated from the cameras. Results indicate a reduction of 50% in the flatness error is achievable and running time of the process is also decreased.