# AirLoc: Object-based Indoor Relocalization

arXiv: https://arxiv.org/abs/2304.00954 · HTML: https://mlsanity.com/paper/2304.00954/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 26 of 26)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Video Instance Segmentation](https://mlsanity.com/paper/1905.04804/) (2019)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1911.04231/) (2019)
- [RIO: 3D Object Instance Re-Localization in Changing Indoor Environments](https://mlsanity.com/paper/1908.06109/) (2019)
- [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017)
- [X-View: Graph-Based Semantic Multi-View Localization](https://mlsanity.com/paper/1709.09905/) (2017)
- [Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment](https://mlsanity.com/paper/2010.09297/) (2020)
- [A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition](https://mlsanity.com/paper/2107.02440/) (2021)
- [Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization](https://mlsanity.com/paper/1710.10519/) (2017)
- [3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning](https://mlsanity.com/paper/2005.06147/) (2020)
- [AirObject: A Temporally Evolving Graph Embedding for Object Identification](https://mlsanity.com/paper/2111.15150/) (2021)
- [Semantic SLAM with Autonomous Object-Level Data Association](https://mlsanity.com/paper/2011.10625/) (2020)
- [AirCode: A Robust Object Encoding Method](https://mlsanity.com/paper/2105.00327/) (2021)
- [An Implementation Approach and Performance Analysis of Image Sensor Based Multilateral Indoor Localization and Navigation System](https://mlsanity.com/paper/1810.02600/) (2018)

## Built on by (most cited first, 2 of 2)

- [Symbolic Graph Inference for Compound Scene Understanding](https://mlsanity.com/paper/2410.22626/) (2024)
- [AnchorVLN: Geometry-Anchored Vision-Language Grounding Reasoning for Open-Vocabulary Navigation](https://mlsanity.com/paper/2609.12285/) (2026)

## Abstract (arXiv)

Indoor relocalization is vital for both robotic tasks like autonomous exploration and civil applications such as navigation with a cell phone in a shopping mall. Some previous approaches adopt geometrical information such as key-point features or local textures to carry out indoor relocalization, but they either easily fail in an environment with visually similar scenes or require many database images. Inspired by the fact that humans often remember places by recognizing unique landmarks, we resort to objects, which are more informative than geometry elements. In this work, we propose a simple yet effective object-based indoor relocalization approach, dubbed AirLoc. To overcome the critical challenges of object reidentification and remembering object relationships, we extract object-wise appearance embedding and inter-object geometric relationships. The geometry and appearance features are integrated to generate cumulative scene features. This results in a robust, accurate, and portable indoor relocalization system, which outperforms the state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7% of accuracy. In addition to exhaustive evaluation, we also carry out real-world tests, where AirLoc shows robustness in challenges like severe occlusion, perceptual aliasing, viewpoint shift, and deformation.