# AirCode: A Robust Object Encoding Method

arXiv: https://arxiv.org/abs/2105.00327 · HTML: https://mlsanity.com/paper/2105.00327/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2021.

## Built on by (most cited first, 4 of 4)

- [AirObject: A Temporally Evolving Graph Embedding for Object Identification](https://mlsanity.com/paper/2111.15150/) (2021)
- [Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search](https://mlsanity.com/paper/2407.07541/) (2024)
- [AirLoc: Object-based Indoor Relocalization](https://mlsanity.com/paper/2304.00954/) (2023)
- [Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency](https://mlsanity.com/paper/2304.05146/) (2023)

## Abstract (arXiv)

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.