Context-aware 6D Pose Estimation of Known Objects using RGB-D data
This work addresses the problem of reliable pose estimation for robotics applications like grasping and navigation, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles 6D object pose estimation in cluttered, occluded scenes by introducing a context-aware architecture that treats symmetric and non-symmetric objects separately, achieving a 3.2% accuracy improvement over the prior state-of-the-art on the LineMOD dataset.
6D object pose estimation has been a research topic in the field of computer vision and robotics. Many modern world applications like robot grasping, manipulation, autonomous navigation etc, require the correct pose of objects present in a scene to perform their specific task. It becomes even harder when the objects are placed in a cluttered scene and the level of occlusion is high. Prior works have tried to overcome this problem but could not achieve accuracy that can be considered reliable in real-world applications. In this paper, we present an architecture that, unlike prior work, is context-aware. It utilizes the context information available to us about the objects. Our proposed architecture treats the objects separately according to their types i.e; symmetric and non-symmetric. A deeper estimator and refiner network pair is used for non-symmetric objects as compared to symmetric due to their intrinsic differences. Our experiments show an enhancement in the accuracy of about 3.2% over the LineMOD dataset, which is considered a benchmark for pose estimation in the occluded and cluttered scenes, against the prior state-of-the-art DenseFusion. Our results also show that the inference time we got is sufficient for real-time usage.