Extrinsic calibration of a single line scanning lidar and a camera

Lidar and visual imagery have been broadly utilized in computer vision and mobile robotics applications because these sensors provide complementary information. However, in order to convert data between the local coordinate systems, we must estimate the rigid body transformation between the sensors. In this paper, we propose a robust-weighted extrinsic calibration algorithm that is implemented easily and has small calibration error. The extrinsic calibration parameters are estimated by minimizing the distance between corresponding features projected onto the image plane. The features are edge and centerline features on a v-shaped calibration target. The proposed algorithm contributes two ways to improve the calibration accuracy. First, we use different weights to distance between a point and a line feature according to the correspondence accuracy of the features. Second, we apply a penalizing function to exclude the influence of outliers in the calibration data sets. We conduct several experiments to evaluate the performance of our extrinsic calibration algorithm, such as comparison of the RMS distance of the ground truth and the projected points, the effect of the number of lidar scan and image, and the effect of pose and range of the calibration target. In the experiments, we show our extrinsic calibration algorithm has calibration accuracy over 50% better than an existing state of the art approach. To evaluate the generality of our algorithm, we also colorize point clouds with different pairs of lidars and cameras calibrated by our algorithm.