# Camera Motion Estimation from RGB-D-Inertial Scene Flow

arXiv: https://arxiv.org/abs/2404.17251 · HTML: https://mlsanity.com/paper/2404.17251/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [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)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields](https://mlsanity.com/paper/2210.13641/) (2022)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM](https://mlsanity.com/paper/1911.05603/) (2019)
- [FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow](https://mlsanity.com/paper/2003.05102/) (2020)
- [Learning Multi-Object Tracking and Segmentation from Automatic Annotations](https://mlsanity.com/paper/1912.02096/) (2019)
- [Neuromorphic Optical Flow and Real-time Implementation with Event Cameras](https://mlsanity.com/paper/2304.07139/) (2023)
- [Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC](https://mlsanity.com/paper/2001.02307/) (2020)
- [Dense RGB-D-Inertial SLAM with Map Deformations](https://mlsanity.com/paper/2207.10940/) (2022)

## Built on by (most cited first, 3 of 3)

- [GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residuals](https://mlsanity.com/paper/2506.11534/) (2025)
- [Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild Video](https://mlsanity.com/paper/2512.12165/) (2025)
- [DefVINS: Visual-Inertial Odometry for Deformable Scenes](https://mlsanity.com/paper/2601.00702/) (2026)

## Abstract (arXiv)

In this paper, we introduce a novel formulation for camera motion estimation that integrates RGB-D images and inertial data through scene flow. Our goal is to accurately estimate the camera motion in a rigid 3D environment, along with the state of the inertial measurement unit (IMU). Our proposed method offers the flexibility to operate as a multi-frame optimization or to marginalize older data, thus effectively utilizing past measurements. To assess the performance of our method, we conducted evaluations using both synthetic data from the ICL-NUIM dataset and real data sequences from the OpenLORIS-Scene dataset. Our results show that the fusion of these two sensors enhances the accuracy of camera motion estimation when compared to using only visual data.