Challenges for Monocular 6-D Object Pose Estimation in Robotics

Object pose estimation is a core perception task that enables, for example, object manipulation and scene understanding. The widely available, inexpensive, and high-resolution RGB sensors and CNNs that allow for fast inference make monocular approaches especially well-suited for robotics applications. We observe that previous surveys establish the state of the art for varying modalities, single- and multiview settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central and largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved.