LIDAR and RADAR are two commonly used sensors in autonomous driving systems. The extrinsic calibration between the two is crucial for effective sensor fusion. The challenge arises due to the low accuracy and sparse information in RADAR measurements. This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems. The method employs simple targets to generate data, including correspondence registration and a one-step optimization algorithm. The optimization aims to minimize the reprojection error while utilizing a small multi-layer perception (MLP) to perform regression on the return energy of the sensor around the targets. The proposed approach uses a deep learning framework such as PyTorch and can be optimized through gradient descent. The experiment uses a 360-degree Ouster-128 LIDAR and a 360-degree Navtech RADAR, providing raw measurements. The results validate the effectiveness of the proposed method in achieving improved estimates of extrinsic calibration parameters.

翻译：激光雷达（LIDAR）和雷达（RADAR）是自动驾驶系统中常用的两种传感器。两者之间的外参标定对有效传感器融合至关重要。由于雷达测量精度低且信息稀疏，标定工作面临挑战。本文提出了一种面向自主系统的3D雷达-激光雷达标定新方案。该方法采用简单目标生成数据，包含对应点配准和单步优化算法。优化目标是最小化重投影误差，同时利用小型多层感知器（MLP）对目标周围传感器的回波能量进行回归。所提方法基于PyTorch等深度学习框架，可通过梯度下降进行优化。实验采用360度Ouster-128激光雷达和360度Navtech雷达获取原始测量数据。结果验证了该方法在提升外参标定参数估计精度方面的有效性。