Pose Estimation of Specular and Symmetrical Objects

TL;DR

This paper introduces a data-driven method for 6D pose estimation of specular, symmetrical objects using monocular RGB images, addressing challenges posed by lack of texture and variable appearance due to lighting and viewpoint.

Contribution

It proposes a novel approach combining CNNs with a specialized cost function for symmetry, enabling effective pose estimation of reflective objects without extensive environment modeling.

Findings

System successfully estimates 6D pose of specular objects

Method outperforms traditional template matching techniques

Feasibility demonstrated for robotic grasping applications

Abstract

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.