Reconstruction-based 6D pose estimation for robotic assembly

Abstract

Pose estimation is important for many robotic applications including bin picking and robotic assembly and collaboration. However, robust and accurate estimation of the poses of industrial objects is a challenging task owing to the various object shapes and complex working environments. This paper presents a method of estimating the poses of narrow and elongated industrial objects with a low-cost RGB-D (depth and color) camera to guide the process of robotic assembly. The proposed method comprises three main steps: reconstruction involved in preprocessing, pose initialization with geometric features, and tracking aided by contour cues. Pose tracking is coupled with real-time dense reconstruction, which can synthesize a smooth depth image as a substitute for the raw depth image. Because industrial objects (e.g., fork and adapter) feature mostly planar structures, primitive geometric features, such as three-dimensional planes, are extracted from the point cloud and utilized to induce a promising initial pose. For robust tracking of the adapter consisting of narrow and elongated planes, the dense surface correspondences are combined with sparse contour correspondences in the refinement scheme. This combination allows for a satisfactory tolerance to the initial guess in the pose tracking phase. The experimental results demonstrate the feasibility of the proposed method.