AirCode: A Robust Object Encoding Method

TL;DR

AirCode introduces a robust object encoding method based on key-point graphs, improving object identification and relocalization in robotic tasks by being resilient to viewpoint, scale, occlusion, and deformation.

Contribution

The paper presents a novel, feature sparse and dense encoding approach that enhances robustness and accuracy in object identification compared to existing methods.

Findings

Achieves superior object identification performance.

Provides reliable semantic relocalization.

Robust to viewpoint, scale, occlusion, and deformation.

Abstract

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.