An Automatic Extrinsic Calibration Method for LiDAR-Camera Fusion via Combining Semantic and Geometric Features

Precise extrinsic calibration is one of the key techniques for LiDAR-camera fusion system. In current methods, the extrinsic calibration is usually not automatic. To address this, an automatic calibration method via combining semantic and geometric features is proposed, which is not dependent on any specific calibration object. First, extrinsics are automatically initialized; semantic objects are utilized to formulate the edge constraints and projection boundary constraints. Then, an efficient global optimization algorithm that synergizes the Jacobian matrix and the stochastic strategy of simulated annealing is put forward to calculate precise extrinsics. A feedback mechanism is designed to evaluate the reliability of the proposed method. Experiments on the KITTI dataset show that the proposed method achieves a rotation error of 0.14°and a translation error of 4.5cm, outperforming most current methods. Besides, the experiment on the proposed optimization algorithm is also conducted to verify its effectiveness and efficiency.