Automatic Extrinsic Calibration of Thermal Camera and LiDAR for Vehicle Sensor Setups

Farhad Dalirani, Farzan Heidari, Taufiq Rahman, Daniel Singh Cheema, Michael A. Bauer
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Abstract

LiDAR is one of the most used sensors in many areas like robotics, self-driving cars, and advanced driving assistance systems due to providing an accurate point cloud of the surroundings. However, to cope with challenges in perceiving the environment around a vehicle, LiDAR data is often combined with data from other sensors. Thermal cameras can provide complementary information that can be beneficial, especially for detecting pedestrians and seeing at nighttime and in fog, dust, etc. In this paper, we propose an algorithm for the extrinsic calibration of a thermal camera and a LiDAR sensor in a vehicle. First, one or more thermal image-point cloud pairs of our designed calibration target are collected. Then line and plane equations of the target’s edges and plane in both data modalities are found. Finally, the algorithm uses lines and plane correspondences to cross-calibrate the sensors. The proposed method obtains good results with one or more poses. We also show that it works well with sparse LiDAR data. Several experiments are presented to illustrate the effectiveness of the method.
车载传感器热像仪和激光雷达的自动外部定标
激光雷达是机器人、自动驾驶汽车和高级驾驶辅助系统等许多领域使用最多的传感器之一,因为它可以提供精确的周围环境点云。然而,为了应对感知车辆周围环境的挑战,激光雷达数据通常与来自其他传感器的数据相结合。热像仪可以提供有益的补充信息,特别是在夜间、雾霾、灰尘等环境中检测行人和观察。本文提出了一种车载热像仪和激光雷达传感器的外部标定算法。首先,收集设计的标定目标的一个或多个热像点云对。然后分别求出两种数据模式下目标边缘和平面直线方程。最后,利用线平面对应对传感器进行交叉标定。该方法在一个或多个姿态下均能获得较好的效果。我们还证明了它可以很好地处理稀疏激光雷达数据。通过实验验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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