# GOReloc: Graph-based Object-Level Relocalization for Visual SLAM

arXiv: https://arxiv.org/abs/2408.07917 · HTML: https://mlsanity.com/paper/2408.07917/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM](https://mlsanity.com/paper/1804.04011/) (2018)
- [X-View: Graph-Based Semantic Multi-View Localization](https://mlsanity.com/paper/1709.09905/) (2017)
- [RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map](https://mlsanity.com/paper/2210.05984/) (2022)
- [OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM](https://mlsanity.com/paper/2209.08338/) (2022)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [SemanticLoop: loop closure with 3D semantic graph matching](https://mlsanity.com/paper/2211.11977/) (2022)
- [VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks](https://mlsanity.com/paper/2402.13609/) (2024)

## Built on by (most cited first, 7 of 7)

- [Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs](https://mlsanity.com/paper/2506.07454/) (2025)
- [Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching](https://mlsanity.com/paper/2409.11555/) (2024)
- [MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization](https://mlsanity.com/paper/2512.03522/) (2025)
- [Compact Object-Level Representations with Open-Vocabulary Understanding for Indoor Visual Relocalization](https://mlsanity.com/paper/2606.24767/) (2026)
- [SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames](https://mlsanity.com/paper/2412.01500/) (2024)
- [Convex Hull-based Algebraic Constraint for Visual Quadric SLAM](https://mlsanity.com/paper/2503.01254/) (2025)
- [VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph](https://mlsanity.com/paper/2609.21804/) (2026)

## Abstract (arXiv)

This article introduces a novel method for object-level relocalization of robotic systems. It determines the pose of a camera sensor by robustly associating the object detections in the current frame with 3D objects in a lightweight object-level map. Object graphs, considering semantic uncertainties, are constructed for both the incoming camera frame and the pre-built map. Objects are represented as graph nodes, and each node employs unique semantic descriptors based on our devised graph kernels. We extract a subgraph from the target map graph by identifying potential object associations for each object detection, then refine these associations and pose estimations using a RANSAC-inspired strategy. Experiments on various datasets demonstrate that our method achieves more accurate data association and significantly increases relocalization success rates compared to baseline methods. The implementation of our method is released at urlhttps://github.com/yutongwangBIT/GOReloc.