Calibration of multi-camera systems, i.e. determining the relative poses between the cameras, is a prerequisite for many tasks in computer vision and robotics. Camera calibration is typically achieved using offline methods that use checkerboard calibration targets. These methods, however, often are cumbersome and lengthy, considering that a new calibration is required each time any camera pose changes. In this work, we propose a novel, marker-free online method for the extrinsic calibration of multiple smart edge sensors, relying solely on 2D human keypoint detections that are computed locally on the sensor boards from RGB camera images. Our method assumes the intrinsic camera parameters to be known and requires priming with a rough initial estimate of the camera poses. The person keypoint detections from multiple views are received at a central backend where they are synchronized, filtered, and assigned to person hypotheses. We use these person hypotheses to repeatedly solve optimization problems in the form of factor graphs. Given suitable observations of one or multiple persons traversing the scene, the estimated camera poses converge towards a coherent extrinsic calibration within a few minutes. We evaluate our approach in real-world settings and show that the calibration with our method achieves lower reprojection errors compared to a reference calibration generated by an offline method using a traditional calibration target.
翻译:多相机系统的校准,即确定相机之间的相对构成,是计算机视觉和机器人中许多任务的一个先决条件。相机校准通常是使用使用校准校准目标的离线方法实现的。然而,这些方法往往很繁琐和冗长,因为每次任何相机出现变化时都需要新的校准。在这项工作中,我们建议一种新型的、无标记的在线方法,用于多智能边缘传感器的外部校准,仅依靠从 RGB 相机图像传感器板上本地计算的2D 人关键点检测。我们的方法假定了内在的相机参数为人所熟知,需要用对相机的粗略初步估计来进行校准。从多个观点中收到的人关键点检测是在中心后端接收的,而它们都是同步、过滤和指派给人假体的。我们用这些人的假说来反复解决因子图形形式的优化问题。根据对一名或多名在现场穿透的传感器进行的适当观测,估计的相机在近几分钟内向一个连贯的极限校准极限的底镜,我们用一个比较的校准方法用一个实际校准方法来显示我们的校准校准的校准方法。