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.
Targetless and automatic 2D-3D calibration methods generally use the common information existing in both image and point cloud. For example, portions that appear as discontinuous 3D shapes are highly likely to appear as edges on an RGB image. Methods that align this three-dimensional discontinuous portion with the 2D edge has been proposed @cite_24 @cite_8 . Meanwhile, a multi-modal alignment method using Mutual Information (MI) was proposed by @cite_2 , and it has been developed mainly in the field of medical imaging. 2D-3D calibration methods using MI for evaluating the commonality between LiDAR and camera have also been proposed in recent years. As indicators evaluated through MI, reflectance - gray scale intensity @cite_3 , surface normal - gray scale intensity @cite_12 , and multiple evaluation indicators including discontinuities of LiDAR data and edge strength in images @cite_19 etc. are used. Taylor and Nieto also proposed gradient based metric in @cite_15 .
@cite_8 @cite_3 @cite_24 @cite_19 @cite_2 @cite_15 @cite_12
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This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard’s patterns. Powell’s method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters. This paper reports a novel mutual information (MI) based algorithm for automatic registration of unstructured 3D point clouds comprised of co-registered 3D lidar and camera imagery. The proposed method provides a robust and principled framework for fusing the complementary information obtained from these two different sensing modalities. High-dimensional features are extracted from a training set of textured point clouds (scans) and hierarchical k-means clustering is used to quantize these features into a set of codewords. Using this codebook, any new scan can be represented as a collection of codewords. Under the correct rigid-body transformation aligning two overlapping scans, the MI between the codewords present in the scans is maximized. We apply a James-Stein-type shrinkage estimator to estimate the true MI from the marginal and joint histograms of the codewords extracted from the scans. Experimental results using scans obtained by a vehicle equipped with a 3D laser scanner and an omnidirectional camera are used to validate the robustness of the proposed algorithm over a wide range of initial conditions. We also show that the proposed method works well with 3D data alone. Many successful feature detectors and descriptors exist for 2D intensity images. However, obtaining the same effectiveness in the domain of 3D objects has proven to be a more elusive goal. In fact, the smoothness often found in surfaces and the lack of texture information on the range images produced by conventional 3D scanners hinder both the localization of interesting points and the distinctiveness of their characterization in terms of descriptors. To overcome these limitations several approaches have been suggested, ranging from the simple enlargement of the area over which the descriptors are computed to the reliance on external texture information. In this paper we offer a change in perspective, where a game-theoretic matching technique that exploits global geometric consistency allows to obtain an extremely robust surface registration even when coupled with simple surface features exhibiting very low distinctiveness. In order to assess the performance of the whole approach we compare it with state-of-the-art alignment pipelines. Furthermore, we show that using the novel feature points with well-known alternative non-global matching techniques leads to poorer results. This paper reports on an algorithm for automatic, targetless, extrinsic calibration of a lidar and optical camera system based upon the maximization of mutual information between the sensor-measured surface intensities. The proposed method is completely data-driven and does not require any fiducial calibration targets-making in situ calibration easy. We calculate the Cramer-Rao lower bound CRLB of the estimated calibration parameter variance, and we show experimentally that the sample variance of the estimated parameters empirically approaches the CRLB when the amount of data used for calibration is sufficiently large. Furthermore, we compare the calibration results to independent ground-truth where available and observe that the mean error empirically approaches zero as the amount of data used for calibration is increased, thereby suggesting that the proposed estimator is a minimum variance unbiased estimate of the calibration parameters. Experimental results are presented for three different lidar-camera systems: i a three-dimensional 3D lidar and omnidirectional camera, ii a 3D time-of-flight sensor and monocular camera, and iii a 2D lidar and monocular camera.