# RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments

arXiv: https://arxiv.org/abs/2310.15072 · HTML: https://mlsanity.com/paper/2310.15072/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 60 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 27 of 27)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM](https://mlsanity.com/paper/2304.14377/) (2023)
- [ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields](https://mlsanity.com/paper/2211.11704/) (2022)
- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://mlsanity.com/paper/1804.09194/) (2018)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [Vox-Surf: Voxel-based Implicit Surface Representation](https://mlsanity.com/paper/2208.10925/) (2022)
- [DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM](https://mlsanity.com/paper/2010.07820/) (2020)
- [A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03638/) (2019)
- [A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03642/) (2019)
- [Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization](https://mlsanity.com/paper/1804.05625/) (2018)
- [Visual-Inertial Monocular SLAM with Map Reuse](https://mlsanity.com/paper/1610.05949/) (2016)
- [PVO: Panoptic Visual Odometry](https://mlsanity.com/paper/2207.01610/) (2022)
- [ADVIO: An authentic dataset for visual-inertial odometry](https://mlsanity.com/paper/1807.09828/) (2018)
- [DM-VIO: Delayed Marginalization Visual-Inertial Odometry](https://mlsanity.com/paper/2201.04114/) (2022)
- [PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features](https://mlsanity.com/paper/2009.07462/) (2020)
- [DynaVINS: A Visual-Inertial SLAM for Dynamic Environments](https://mlsanity.com/paper/2208.11500/) (2022)
- [Robocentric Visual-Inertial Odometry](https://mlsanity.com/paper/1805.04031/) (2018)
- [RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments](https://mlsanity.com/paper/2304.10987/) (2023)
- [RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments](https://mlsanity.com/paper/2103.10400/) (2021)

## Built on by (most cited first, 9 of 9)

- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment](https://mlsanity.com/paper/2403.18274/) (2024)
- [CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation](https://mlsanity.com/paper/2602.15060/) (2026)
- [EGG-Fusion: Efficient 3D Reconstruction with Geometry-aware Gaussian Surfel on the Fly](https://mlsanity.com/paper/2512.01296/) (2025)
- [Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing](https://mlsanity.com/paper/2508.13797/) (2025)
- [Generative Augmented Reality: Paradigms, Technologies, and Future Applications](https://mlsanity.com/paper/2511.16783/) (2025)
- [STAG-VIO: Stabilized Prompt-to-Geometry Interface for Robust Dynamic Visual--Inertial Odometry](https://mlsanity.com/paper/2411.19289/) (2024)
- [Understanding Sensor Vulnerabilities in Industrial XR Tracking](https://mlsanity.com/paper/2602.14413/) (2026)
- [Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints](https://mlsanity.com/paper/2606.06877/) (2026)

## Abstract (arXiv)

It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Secondly, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets and online comparison. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments. The source code is available at: hrefhttps://github.com/openxrlab/xrslamfontfamilypcrselectfont https://github.com/openxrlab/xrslam.