A comparison and analysis of RGB-D cameras' depth performance for robotics application

Abstract

Consumer-grade RGB-D cameras capture RGB images along with per-pixel depth information, and because of their limited cost and ability to measure distances at a high frame rate, have been used in robotics and computer vision application. However, drawbacks include the repeatability and accuracy of RGB-D cameras for object detection and localization. This paper investigates and compares RGB-D cameras' performance in terms of depth image quality, depth clouds distribution, etc. performance and configuration methods of frequently used cameras, e.g. PrimeSense, Kinect V1 and Kinect V2, in order to provide useful advice when choosing a camera for robotic applications. Experimental and Point Cloud Library (PCL)-based methods are introduced for point-to-plane distance detection. Based on the obtained results, a relationship between measurements and ground truth is built.