Pose Estimation of Specular and Symmetrical Objects
This addresses a long-standing challenge in robotics for handling ubiquitous specular and textureless metallic components, though it appears incremental by building on existing CNN-based methods.
The paper tackles the problem of 6D pose estimation for specular and symmetrical objects using only a monocular RGB camera, proposing a data-driven solution with a cost function for symmetry handling and demonstrating experimental results that show the system's feasibility.
In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because of the absence of rich texture features. Furthermore, the appearance of specularity heavily depends on the camera viewpoint and environmental light conditions making traditional methods, like template matching, fail. In the last 30 years, pose estimation of the specular object has been a consistent challenge, and most related works require massive knowledge modeling effort for light setups, environment, or the object surface. On the other hand, recent works exhibit the feasibility of 6D pose estimation on a monocular camera with convolutional neural networks(CNNs) however they mostly use opaque objects for evaluation. This paper provides a data-driven solution to estimate the 6D pose of specular objects for grasping them, proposes a cost function for handling symmetry, and demonstrates experimental results showing the system's feasibility.