Challenges for Monocular 6D Object Pose Estimation in Robotics

TL;DR

This paper reviews the specific challenges of monocular 6D object pose estimation in robotics, highlighting open problems like occlusion, pose representation, and category-level estimation, and suggests future research directions.

Contribution

It provides a unified analysis of recent works, identifying key open challenges unique to monocular approaches in robotics applications.

Findings

Occlusion handling remains a major challenge.

Category-level pose estimation needs formalization and improvement.

Handling large object sets and uncertainty is largely unsolved.

Abstract

Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference based on this modality make monocular approaches especially well suited for robotics applications. We observe that previous surveys on object pose estimation establish the state of the art for varying modalities, single- and multi-view 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, novel 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, 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.