AirCode: ロバストな物体エンコーディング手法
AirCode: A Robust Object Encoding Method
キーポイントのグラフに基づく物体エンコーディング手法AirCodeを提案し、視点変化や遮蔽に頑健で物体識別と意味的再定位に優れることを示した。
著者: Kuan Xu, Chen Wang, Chao Chen, Wei Wu, Sebastian Scherer
分類: cs.RO
原文アブストラクト
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.
