Multi-modal sensory data plays an important role in many computer vision and robotics tasks. One popular multi-modal pair is cameras and laser scanners. To overlay and jointly use the data from both modalities, it is necessary to calibrate the sensors, i.e., to obtain the spatial relation between the sensors. Computing such a calibration is challenging as both sensors provide quite different data: cameras yield color or brightness information, laser scanners yield 3-D points. However, several laser scanners additionally provide reflectances, which turn out to make calibration to a camera well feasible. To this end, we first estimate a rough alignment of the coordinate systems of both modalities. Then, we use the laser scanner reflectances to compute a virtual image of the scene. Stereo calibration on the virtual image and the camera image are then used to compute a refined, high-accuracy calibration. It is encouraging that the accuracies in our experiments are comparable to camera-camera stereo setups and outperform another of other target-based calibration approach. This shows that the proposed algorithm reliably integrates the point cloud with the intensity image. As an example application, we use the calibration results to obtain ground-truth distance images for range cameras. Furthermore, we utilize this data to investigate the accuracy of the Microsoft Kinect V2 time-of-flight and the Intel RealSense R200 structured light camera.

中文翻译：

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精确的激光扫描仪可对相机进行校准，并适用于范围传感器评估

多模式感官数据在许多计算机视觉和机器人技术任务中起着重要作用。一种流行的多模式对是照相机和激光扫描仪。为了覆盖和联合使用来自两种模态的数据，必须校准传感器，即获得传感器之间的空间关系。由于两个传感器都提供完全不同的数据，因此计算这样的校准具有挑战性：相机产生颜色或亮度信息，激光扫描仪产生3D点。但是，一些激光扫描仪还提供了反射率，这使对相机的校准变得非常可行。为此，我们首先估计两种方式的坐标系的粗略对齐。然后，我们使用激光扫描仪的反射率来计算场景的虚拟图像。然后，使用虚拟图像和摄像机图像上的立体校准来计算精确的高精度校准。令人鼓舞的是，我们的实验精度可与相机-相机立体声设置媲美，并且优于其他基于目标的其他校准方法。这表明所提出的算法可靠地将点云与强度图像集成在一起。作为示例应用程序，我们使用校准结果来获取测距相机的真实距离图像。此外，我们利用这些数据来调查Microsoft Kinect V2飞行时间和英特尔实感R200结构化摄像头的准确性。令人鼓舞的是，我们的实验精度可与相机-相机立体声设置媲美，并且优于其他基于目标的其他校准方法。这表明所提出的算法可靠地将点云与强度图像集成在一起。作为示例应用程序，我们使用校准结果来获取测距相机的真实距离图像。此外，我们利用这些数据来调查Microsoft Kinect V2飞行时间和英特尔实感R200结构化摄像头的准确性。令人鼓舞的是，我们的实验精度可与相机-相机立体声设置媲美，并且优于其他基于目标的其他校准方法。这表明所提出的算法可靠地将点云与强度图像集成在一起。作为示例应用程序，我们使用校准结果来获取测距相机的真实距离图像。此外，我们利用这些数据来调查Microsoft Kinect V2飞行时间和英特尔实感R200结构化摄像头的准确性。