  Object encoding and identification are crucial for many robotic tasks such as
autonomous exploration and semantic relocalization. Existing works heavily rely
on the tracking of detected objects but have difficulty recalling revisited
objects precisely. In this paper, we propose a novel object encoding method,
which is named as AirCode, based on a graph of key-points. To be robust to the
number of key-points detected, we propose a feature sparse encoding and object
dense encoding method to ensure that each key-point can only affect a small
part of the object descriptors, leading it to be robust to viewpoint changes,
scaling, occlusion, and even object deformation. In the experiments, we show
that it achieves superior performance for object identification than the
state-of-the-art algorithms and is able to provide reliable semantic
relocalization. It is a plug-and-play module and we expect that it will play an
important role in various applications.
