  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.
