Real-time Pose Estimation of Rigid Objects using RGB-D Imagery

Using full scale (480x640) RGB-D imagery, we here present an approach for tracking 6d pose of rigid objects at runtime frequency of up to 15fps. This approach is useful for robotic perception systems to efficiently track object's pose during camera movements in tabletop manipulation tasks with high detection rate and real-time performance. Specifically, appearance-based feature correspondences are used for initial object detection. We make use of Oriented Brief (ORB) feature key-points to perform fast segmentation of object candidates in the 3d point cloud. The task of 6d pose estimation is handled in the Cartesian space by finding an interest window around the segmented object and 3d geometry operations. The interest window is later used for feature extraction in the subsequent camera frames to speed up the object detection process. This also allows for an efficient pose tracking of scenes where there are significantly large false matches between feature correspondences due to scene clutter. Our approach was tested using an RGB-D dataset comprising of scenes from video sequences of tabletops with multiple objects in household environments. Experiments show that our approach is capable of performing 3d segmentation followed by 6d pose tracking at higher frame rates compared to existing techniques.