Abstract
Machine vision and image processing are often used with sensors for situation awareness in autonomous systems, from industrial robots to self-driving cars. The 3D depth sensors, such as LiDAR (Light Detection and Ranging), Radar, are great invention for autonomous systems. Due to the complexity of the setup, LiDAR may not be suitable for some operational environments, for example, a space environment. This study was motivated by a desire to get real-time volumetric and change information with multiple 2D cameras instead of a depth camera. Two cameras were used to measure the dimensions of a rectangular object in real-time. The R-C-P (row-column-pixel) method is developed using image processing and edge detection. In addition to the surface areas, the R-C-P method also detects discontinuous edges or volumes. Lastly, experimental work is presented for illustration of the R-C-P method, which provides the equations for calculating surface area dimensions. Using the equations with given distance information between the object and the camera, the vision system provides the dimensions of actual objects.
Abstract (translated)
机器视觉和图像处理通常与传感器一起用于自主系统的环境感知，从工业机器人到自动驾驶汽车。3D深度传感器（如激光雷达，雷达）是自主系统的伟大发明。由于设置的复杂性，激光雷达可能不适合某些操作环境，例如空间环境。本研究旨在通过使用多个2D相机来获取实时的体积和变化信息，而不是使用深度相机。两个相机用于实时测量一个矩形物体的尺寸。利用图像处理和边缘检测，开发了R-C-P（行-列-像素）方法。除了表面面积，R-C-P方法还检测到不连续的边缘或体积。最后，以实验工作为例，展示了R-C-P方法，该方法提供了计算表面面积大小的方程。利用给定的物体与相机之间的距离信息，视觉系统提供了实际对象的尺寸。
URL
https://arxiv.org/abs/2308.10058
