Empirical Comparison of Four Stereoscopic Depth Sensing Cameras for Robotics Applications

TL;DR

This study empirically compares four stereoscopic RGB-D cameras across various scenarios, revealing their strengths and limitations for robotics applications, with detailed performance data available publicly.

Contribution

It provides a comprehensive empirical evaluation of four stereoscopic depth cameras for robotics, highlighting their performance differences and suitability for various distances and applications.

Findings

D435 performs best up to 1 meter with under 1 cm error

ZED 2 maintains under 3 cm error at 4 meters

OAK-D Pro offers integrated AI modules

Abstract

Depth sensing is an essential technology in robotics and many other fields. Many depth sensing (or RGB-D) cameras are available on the market and selecting the best one for your application can be challenging. In this work, we tested four stereoscopic RGB-D cameras that sense the distance by using two images from slightly different views. We empirically compared four cameras (Intel RealSense D435, Intel RealSense D455, StereoLabs ZED 2, and Luxonis OAK-D Pro) in three scenarios: (i) planar surface perception, (ii) plastic doll perception, (iii) household object perception (YCB dataset). We recorded and evaluated more than 3,000 RGB-D frames for each camera. For table-top robotics scenarios with distance to objects up to one meter, the best performance is provided by the D435 camera that is able to perceive with an error under 1 cm in all of the tested scenarios. For longer distances, the other three models perform better, making them more suitable for some mobile robotics applications. OAK-D Pro additionally offers integrated AI modules (e.g., object and human keypoint detection). ZED 2 is overall the best camera which is able to keep the error under 3 cm even at 4 meters. However, it is not a standalone device and requires a computer with a GPU for depth data acquisition. All data (more than 12,000 RGB-D frames) are publicly available at https://rustlluk.github.io/rgbd-comparison.