SLATCalib: Extrinsic Calibration between a Sparse 3D LiDAR and a LimitedFOV Lowresolution Thermal Camera Accurate estimation of extrinsic parameter (rotation matrix and translation vector) between heterogeneous sensors is important for fusing complementary information. However, the extrinsic calibration between a sparse 3D LiDAR and a thermal camera is challenging, mainly because of the difficulties to accurately extract common features from a sparse point cloud and a thermal image which has limitedFOV and lowresolution. Previous methods either rely on a dense depth sensor or a visual camera to facilitate the feature extraction process. To address the problem, SLATCalib (Sparse Lidar And Thermal camera Calibration) is proposed. By observing that circular holes could be detected from both sensors, a specially designed calibration board (a rectangular board with four circular holes) is introduced. Four circle centers in 3D space are used as common features. The benefit is point features are accurate and reliable for feature matching. To extract four circle centers from the thermal camera, three steps are carried out: First, a method is proposed to accurately detect the four circles. Then, the homography matrix of the calibration board can be figured out. Lastly, 3D coordinates of the circle centers are calculated by decomposing the homography matrix. From the LiDAR frame, the four circle centers can be segmented out as long as two laser beams pass through each circle. At last, optimal extrinsic parameter is calculated by minimizing the matching error between the four pairs of 3D circle centers. Quantitative and qualitative experiments are carried out. In simulation, SLATCalib outperforms two methods by a large margin. In real environment, it achieves a reprojection error (RMSE) of 0.62 pixel.

Automatic Extrinsic Calibration of LIDAR and Monocular Camera Images Mobile robots are being equipped with an ever increasing amount of sensors, and by taking advantage of each modalityxe2x80x99s forte, through sensor fusion we are able to produce superior results for higher level functions such as pedestrian detection. An accurate calibration between the sensors, however, is essential for obtaining a correct fusion. One of the more common setups is that of an optical camera paired with a lidar system, complementing each otherxe2x80x99s data. While the camera offers rich color information through a dense image, the lidar produces high accuracy, though sparse depth measurements. In this paper we will present two methods for estimating the 6 degrees of freedom parameters necessary to extrinsically calibrate the two sensors, without the use of special calibration targets: the first one attempts to align edges detected in both the lidar point cloud and the corresponding color image, while the second approach minimizes the depth difference between the measured lidar data and a depth map generated from the corresponding monocular image. By defining correlations between the two modalityxe2x80x99s information, we are able to construct an optimization problem on the 6 calibration parameters. Our experiments show good results when matched against similar solutions proposed in the literature, especially in the case of the calibration algorithm based on depth estimation.