Pose estimation is a basic module in many robot manipulation pipelines. Estimating the pose of objects in the environment can be useful for grasping, motion planning, or manipulation. However, current state-of-the-art methods for pose estimation either rely on large annotated training sets or simulated data. Further, the long training times for these methods prohibit quick interaction with novel objects. To address these issues, we introduce a novel method for zero-shot object pose estimation in clutter. Our approach uses a hypothesis generation and scoring framework, with a focus on learning a scoring function that generalizes to objects not used for training. We achieve zero-shot generalization by rating hypotheses as a function of unordered point differences. We evaluate our method on challenging datasets with both textured and untextured objects in cluttered scenes and demonstrate that our method significantly outperforms previous methods on this task. We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation. Our work allows users to estimate the pose of novel objects without requiring any retraining.
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NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction
Recent advances in object segmentation have
demonstrated that deep neural networks excel at object
segmentation for specific classes in color and depth images.
However, their performance is dictated by the number of classes
and objects used for training, thereby hindering generalization
to never seen objects or zero-shot samples. To exacerbate the
problem further, object segmentation using image frames rely
on recognition and pattern matching cues. Instead, we utilize
the ‘active’ nature of a robot and their ability to ‘interact’ with
the environment to induce additional geometric constraints for
segmenting zero-shot samples.
In this paper, we present the first framework to segment
unknown objects in a cluttered scene by repeatedly ‘nudging’ at
the objects and moving them to obtain additional motion cues at
every step using only a monochrome monocular camera. We call
our framework NudgeSeg. These motion cues are used to refine
the segmentation masks. We successfully test our approach to
segment novel objects in various cluttered scenes and provide an
extensive study with image and motion segmentation methods.
We show an impressive average detection rate of over 86% on
zero-shot objects.
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- Award ID(s):
- 1824198
- NSF-PAR ID:
- 10376846
- Date Published:
- Journal Name:
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 2714 to 2712
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS51168.2021.9635832
