A Long-focus Camera and Telemetry LiDAR Calibration for Rapid Transit Equipment.

The fused use of the long-focus camera and telemetry LiDAR is necessary for advanced long-distance detection requirements in autonomous driving vehicles and unmanned subways, and the extrinsic parameter calibration is a precondition for multisensor fusion use. However, the inaccuracy of longfocus camera detection and low accuracy of data association between LiDAR-camera make the calibration a challenging problem. In this paper, we propose a novel calibration method that treat the detection poses as variables in the optimization framework, which significantly reduces the impact of detection errors on calibration accuracy. Furthermore, we construct 3 error terms to obtain better estimation of the variables, and the designed error terms only require plane-toplane data association, avoiding incorrect data association that leads to failure in extrinsic parameter estimation. Then we made a full comparison with other methods through real-world experiments, which showed that our method achieves the best calibration result.