Corpus ID: 198147345

Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter

@article{Price2019InferringOG,
  title={Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter},
  author={Andrew Price and Linyi Jin and Dmitry Berenson},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.08770}
}
Object search -- the problem of finding a target object in a cluttered scene -- is essential to solve for many robotics applications in warehouse and household environments. However, cluttered environments entail that objects often occlude one another, making it difficult to segment objects and infer their shapes and properties. Instead of relying on the availability of CAD or other explicit models of scene objects, we augment a manipulation planner for cluttered environments with a state-of… Expand
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