In this paper, we propose a method of targetless and automatic Camera-LiDAR calibration. Our approach is an extension of hand-eye calibration framework to 2D-3D calibration. By using the sensor fusion odometry method, the scaled camera motions are calculated with high accuracy. In addition to this, we clarify the suitable motion for this calibration method. The proposed method only requires the three-dimensional point cloud and the camera image and does not need other information such as reflectance of LiDAR and to give initial extrinsic parameter. In the experiments, we demonstrate our method using several sensor configurations in indoor and outdoor scenes to verify the effectiveness. The accuracy of our method achieves more than other comparable state-of-the-art methods.
While these methods align 3D point cloud to 2D image using 3D to 2D projections, some texturing methods using images taken from multiple places with a camera to construct a stereo, reconstructing 3d structure from the images and aligning it to 3D point cloud has also been proposed. In @cite_16 , a method of computing the extrinsic parameter between the LiDAR and the camera for the texturing a dense 3D scan is proposed. The extrinsic calibration was done by aligning dense three-dimensional range data and sparse three-dimensional data reconstructed from two-dimensional stereo images.
@cite_16
2047111594
We propose a semi-automatic omnidirectional texturing method that maps a spherical image onto a dense 3D model obtained by a range sensor. For accurate texturing, accurate estimation of the extrinsic parameters is inevitable. In order to estimate these parameters, we propose a robust 3D registration-based method between a dense range data set and a sparse spherical image stereo data set. For measuring the distances between the two data sets, we introduce generalized distances taking account of 3D error distributions of the stereo data. To reconstruct 3D models by images, we use two spherical images taken at arbitrary positions in arbitrary poses. Then, we propose a novel rectification method for spherical images that is derived from E matrix and facilitates the estimation of the disparities. The experimental results show that the proposed method can map the spherical image onto the dense 3D models effectively and accurately.