# Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation

arXiv: https://arxiv.org/abs/2205.01823 · HTML: https://mlsanity.com/paper/2205.01823/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2022.

## Built on by (most cited first, 28 of 28)

- [BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects](https://mlsanity.com/paper/2303.14158/) (2023)
- [NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild](https://mlsanity.com/paper/2405.18715/) (2024)
- [HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation](https://mlsanity.com/paper/2303.15743/) (2023)
- [Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation](https://mlsanity.com/paper/2412.06781/) (2024)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects](https://mlsanity.com/paper/2403.20318/) (2024)
- [Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation](https://mlsanity.com/paper/2308.10016/) (2023)
- [Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors](https://mlsanity.com/paper/2309.09118/) (2023)
- [GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement](https://mlsanity.com/paper/2404.11139/) (2024)
- [Symmetry Strikes Back: From Single-Image Symmetry Detection to 3D Generation](https://mlsanity.com/paper/2411.17763/) (2024)
- [Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation](https://mlsanity.com/paper/2211.00960/) (2022)
- [Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation](https://mlsanity.com/paper/2303.11516/) (2023)
- [CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector](https://mlsanity.com/paper/2508.11185/) (2025)
- [Multi-view object pose estimation from correspondence distributions and epipolar geometry](https://mlsanity.com/paper/2210.00924/) (2022)
- [SUP-NeRF: A Streamlined Unification of Pose Estimation and NeRF for Monocular 3D Object Reconstruction](https://mlsanity.com/paper/2403.15705/) (2024)
- [Symmetry Understanding of 3D Shapes via Chirality Disentanglement](https://mlsanity.com/paper/2508.05505/) (2025)
- [Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation](https://mlsanity.com/paper/2507.00984/) (2025)
- [Object-based SLAM utilizing unambiguous pose parameters considering general symmetry types](https://mlsanity.com/paper/2303.07872/) (2023)
- [Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks](https://mlsanity.com/paper/2309.09206/) (2023)
- [ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization](https://mlsanity.com/paper/2401.08937/) (2024)
- [MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objects](https://mlsanity.com/paper/2603.22839/) (2026)
- [Recursive Cross-View: Use Only 2D Detectors to Achieve 3D Object Detection without 3D Annotations](https://mlsanity.com/paper/2211.07108/) (2022)
- [Category-level Meta-learned NeRF Priors for Efficient Object Mapping](https://mlsanity.com/paper/2503.01582/) (2025)
- [Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation](https://mlsanity.com/paper/2509.01583/) (2025)
- [DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects](https://mlsanity.com/paper/2510.10933/) (2025)
- [Symmetry Matters: Auditing and Symmetrizing 3D Generative Models](https://mlsanity.com/paper/2512.18953/) (2025)
- [ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild](https://mlsanity.com/paper/2604.22202/) (2026)
- [Temporally Consistent Object 6D Pose Estimation for Robot Control](https://mlsanity.com/paper/2605.02708/) (2026)

## Abstract (arXiv)

We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our knowledge, our system is among the first to utilize the camera pose information from SLAM to provide prior knowledge for tracking keypoints on symmetric objects -- ensuring that new measurements are consistent with the current 3D scene. Moreover, our semantic keypoint network is trained to predict the Gaussian covariance for the keypoints that captures the true error of the prediction, and thus is not only useful as a weight for the residuals in the system's optimization problems, but also as a means to detect harmful statistical outliers without choosing a manual threshold. Experiments show that our method provides competitive performance to the state of the art in 6DoF object pose estimation, and at a real-time speed. Our code, pre-trained models, and keypoint labels are available https://github.com/rpng/suo_slam.