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Automatic Extrinsic Calibration of Monocular Camera and LIDAR in Natural Scenes

机译:自然场景中单眼相机和激光雷达的自动外部校准

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It has practical significance to automatically calibrate the multiple sensors in autonomous driving vehicles. This paper proposes an automatic calibration method to address the extrinsic calibration for a perception system comprised a LIDAR and a monocular camera, which can be used in natural scenes without calibration boards. The calibration problem is formulated as minimizing the structure feature errors via nonlinear least squares optimization. In order to solve the nonlinear optimization problem, the continuous frames information are used to initialize parameters and extract features. Simultaneously, Levenberg-Marquardt algorithm and a global optimization algorithm are combined to obtain the best optimal parameters. Extensive experiments tested on KITTI benchmark demonstrate that our method reaches a valid and competitive result.
机译:自动校准自动驾驶汽车中的多个传感器具有实际意义。本文提出了一种自动校准方法,以解决包括LIDAR和单眼相机的感知系统的外部校准,该校准系统可以在没有校准板的自然场景中使用。校准问题被表述为通过非线性最小二乘法优化来最小化结构特征误差。为了解决非线性优化问题,连续帧信息用于初始化参数和提取特征。同时,将Levenberg-Marquardt算法和全局优化算法结合起来,以获得最佳的最优参数。在KITTI基准测试中进行的大量实验表明,我们的方法达到了有效且具有竞争力的结果。

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