# SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation

arXiv: https://arxiv.org/abs/1911.09968 · HTML: https://mlsanity.com/paper/1911.09968/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 24 of 24)

- [Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data](https://mlsanity.com/paper/2206.02353/) (2022)
- [DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment](https://mlsanity.com/paper/2403.18274/) (2024)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection](https://mlsanity.com/paper/2205.06187/) (2022)
- [Monocular Visual-Inertial Depth Estimation](https://mlsanity.com/paper/2303.12134/) (2023)
- [Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach](https://mlsanity.com/paper/2305.06121/) (2023)
- [Physical Passive Patch Adversarial Attacks on Visual Odometry Systems](https://mlsanity.com/paper/2207.05729/) (2022)
- [EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention](https://mlsanity.com/paper/2209.08490/) (2022)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Learned Monocular Depth Priors in Visual-Inertial Initialization](https://mlsanity.com/paper/2204.09171/) (2022)
- [CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry](https://mlsanity.com/paper/2208.13935/) (2022)
- [RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments](https://mlsanity.com/paper/2103.10400/) (2021)
- [LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation](https://mlsanity.com/paper/2209.08816/) (2022)
- [Causal Transformer for Fusion and Pose Estimation in Deep Visual Inertial Odometry](https://mlsanity.com/paper/2409.08769/) (2024)
- [DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusion](https://mlsanity.com/paper/2509.06023/) (2025)
- [Learning Interpretable BEV Based VIO without Deep Neural Networks](https://mlsanity.com/paper/2109.12292/) (2021)
- [Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression](https://mlsanity.com/paper/2208.00919/) (2022)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization](https://mlsanity.com/paper/2401.13076/) (2024)
- [Depth-Consistent 3D Gaussian Splatting via Physical Defocus Modeling and Multi-View Geometric Supervision](https://mlsanity.com/paper/2511.10316/) (2025)
- [SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery](https://mlsanity.com/paper/2211.08904/) (2022)
- [Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images](https://mlsanity.com/paper/2507.15496/) (2025)
- [Dual-Agent Reinforcement Learning for Adaptive and Cost-Aware Visual-Inertial Odometry](https://mlsanity.com/paper/2511.21083/) (2025)
- [DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation](https://mlsanity.com/paper/2607.23755/) (2026)

## Abstract (arXiv)

In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimation. To overcome the data limitation, self-supervised learning has emerged as a promising alternative, exploiting constraints such as geometric and photometric consistency in the scene. In this study, we introduce a novel self-supervised deep learning-based VIO and depth map recovery approach (SelfVIO) using adversarial training and self-adaptive visual-inertial sensor fusion. SelfVIO learns to jointly estimate 6 degrees-of-freedom (6-DoF) ego-motion and a depth map of the scene from unlabeled monocular RGB image sequences and inertial measurement unit (IMU) readings. The proposed approach is able to perform VIO without the need for IMU intrinsic parameters and/or the extrinsic calibration between the IMU and the camera. estimation and single-view depth recovery network. We provide comprehensive quantitative and qualitative evaluations of the proposed framework comparing its performance with state-of-the-art VIO, VO, and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI, EuRoC and Cityscapes datasets. Detailed comparisons prove that SelfVIO outperforms state-of-the-art VIO approaches in terms of pose estimation and depth recovery, making it a promising approach among existing methods in the literature.