  This paper considers online object-level mapping using partial point-cloud
observations obtained online in an unknown environment. We develop and approach
for fully Convolutional Object Retrieval and Symmetry-AIded Registration
(CORSAIR). Our model extends the Fully Convolutional Geometric Features model
to learn a global object-shape embedding in addition to local point-wise
features from the point-cloud observations. The global feature is used to
retrieve a similar object from a category database, and the local features are
used for robust pose registration between the observed and the retrieved
object. Our formulation also leverages symmetries, present in the object
shapes, to obtain promising local-feature pairs from different symmetry classes
for matching. We present results from synthetic and real-world datasets with
different object categories to verify the robustness of our method.
