Quantitative Depth Quality Assessment of RGBD Cameras At Close Range Using 3D Printed Fixtures

TL;DR

This paper introduces a quantitative method for assessing RGBD camera depth quality at close range using 3D printed fixtures, enabling better camera selection for robotic applications in real-world cluttered environments.

Contribution

It presents a novel, extendable measurement approach based on point cloud density and RMSE, bridging the gap between manufacturer metrics and real-world performance.

Findings

Compared three RGBD cameras and evaluated their depth quality.

Provided a case study demonstrating camera selection process.

Shared reference meshes and analysis code for reproducibility.

Abstract

Mobile robots that manipulate their environments require high-accuracy scene understanding at close range. Typically this understanding is achieved with RGBD cameras, but the evaluation process for selecting an appropriate RGBD camera for the application is minimally quantitative. Limited manufacturer-published metrics do not translate to observed quality in real-world cluttered environments, since quality is application-specific. To bridge the gap, we present a method for quantitatively measuring depth quality using a set of extendable 3D printed fixtures that approximate real-world conditions. By framing depth quality as point cloud density and root mean square error (RMSE) from a known geometry, we present a method that is extendable by other system integrators for custom environments. We show a comparison of 3 cameras and present a case study for camera selection, provide reference meshes and analysis code, and discuss further extensions.