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In the robotic industry, specular and textureless metallic components areubiquitous. The 6D pose estimation of such objects with only a monocular RGBcamera is difficult because of the absence of rich texture features.Furthermore, the appearance of specularity heavily depends on the cameraviewpoint and environmental light conditions making traditional methods, liketemplate matching, fail. In the last 30 years, pose estimation of the specularobject has been a consistent challenge, and most related works require massiveknowledge modeling effort for light setups, environment, or the object surface.On the other hand, recent works exhibit the feasibility of 6D pose estimationon a monocular camera with convolutional neural networks(CNNs) however theymostly use opaque objects for evaluation. This paper provides a data-drivensolution to estimate the 6D pose of specular objects for grasping them,proposes a cost function for handling symmetry, and demonstrates experimentalresults showing the system's feasibility.

Pose Estimation of Specular and Symmetrical Objects