# AirDOS: Dynamic SLAM benefits from Articulated Objects

arXiv: https://arxiv.org/abs/2109.09903 · HTML: https://mlsanity.com/paper/2109.09903/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM](https://mlsanity.com/paper/1804.04011/) (2018)
- [VDO-SLAM: A Visual Dynamic Object-aware SLAM System](https://mlsanity.com/paper/2005.11052/) (2020)
- [Dynamic SLAM: The Need For Speed](https://mlsanity.com/paper/2002.08584/) (2020)
- [Robust Dense Mapping for Large-Scale Dynamic Environments](https://mlsanity.com/paper/1905.02781/) (2019)
- [Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions](https://mlsanity.com/paper/1808.00274/) (2018)
- [Towards Real-time Semantic RGB-D SLAM in Dynamic Environments](https://mlsanity.com/paper/2104.01316/) (2021)
- [Non-Iterative SLAM](https://mlsanity.com/paper/1701.05294/) (2017)
- [Dynamic Body VSLAM with Semantic Constraints](https://mlsanity.com/paper/1504.07269/) (2015)

## Built on by (most cited first, 21 of 21)

- [LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry](https://mlsanity.com/paper/2401.01887/) (2024)
- [Any4D: Unified Feed-Forward Metric 4D Reconstruction](https://mlsanity.com/paper/2512.10935/) (2025)
- [DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation](https://mlsanity.com/paper/2411.04999/) (2024)
- [DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments](https://mlsanity.com/paper/2209.08430/) (2022)
- [Dynamic Camera Poses and Where to Find Them](https://mlsanity.com/paper/2504.17788/) (2025)
- [BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking](https://mlsanity.com/paper/2205.02301/) (2022)
- [Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2504.14516/) (2025)
- [MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry](https://mlsanity.com/paper/2409.09479/) (2024)
- [Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy](https://mlsanity.com/paper/2406.16087/) (2024)
- [PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments](https://mlsanity.com/paper/2411.15800/) (2024)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025)
- [DynamicVerse: A Physically-Aware Multimodal Framework for 4D World Modeling](https://mlsanity.com/paper/2512.03000/) (2025)
- [Video Understanding: From Geometry and Semantics to Unified Models](https://mlsanity.com/paper/2603.17840/) (2026)
- [Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments](https://mlsanity.com/paper/2506.05965/) (2025)
- [Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments](https://mlsanity.com/paper/2602.21967/) (2026)
- [Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM](https://mlsanity.com/paper/2504.04844/) (2025)
- [UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling](https://mlsanity.com/paper/2309.17036/) (2023)
- [A Stereo Visual SLAM System Using Object-Level Motion Estimation and Geometric Filtering Based on Cross Disparity](https://mlsanity.com/paper/2607.02005/) (2026)
- [Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAM](https://mlsanity.com/paper/2609.27509/) (2026)
- [A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs](https://mlsanity.com/paper/2610.08800/) (2026)

## Abstract (arXiv)

Dynamic Object-aware SLAM (DOS) exploits object-level information to enable robust motion estimation in dynamic environments. Existing methods mainly focus on identifying and excluding dynamic objects from the optimization. In this paper, we show that feature-based visual SLAM systems can also benefit from the presence of dynamic articulated objects by taking advantage of two observations: (1) The 3D structure of each rigid part of articulated object remains consistent over time; (2) The points on the same rigid part follow the same motion. In particular, we present AirDOS, a dynamic object-aware system that introduces rigidity and motion constraints to model articulated objects. By jointly optimizing the camera pose, object motion, and the object 3D structure, we can rectify the camera pose estimation, preventing tracking loss, and generate 4D spatio-temporal maps for both dynamic objects and static scenes. Experiments show that our algorithm improves the robustness of visual SLAM algorithms in challenging crowded urban environments. To the best of our knowledge, AirDOS is the first dynamic object-aware SLAM system demonstrating that camera pose estimation can be improved by incorporating dynamic articulated objects.