R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision

TL;DR

The paper introduces the R-C-P method, a real-time volume calculation technique using image processing and machine vision with 2D cameras, offering an alternative to depth sensors like LiDAR.

Contribution

It develops a novel R-C-P method that calculates object volume and surface area using 2D images, edge detection, and geometric equations, suitable for various environments.

Findings

Successfully measured object dimensions in real-time using two cameras.

The R-C-P method detects discontinuous edges and volumes.

Provides equations for calculating actual object dimensions from image data.

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.