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.

为了解决 RGBD 相机的选用问题，该研究提出了一种用于定量测量深度质量的方法，并通过比较不同相机的性能和案例研究帮助其他系统集成商选择适合定制环境的相机。