# Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning

arXiv: https://arxiv.org/abs/2405.16754 · HTML: https://mlsanity.com/paper/2405.16754/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2024.

## Built on by (most cited first, 4 of 4)

- [VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting](https://mlsanity.com/paper/2412.02503/) (2024)
- [An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open World](https://mlsanity.com/paper/2504.11698/) (2025)
- [Dual-Agent Reinforcement Learning for Adaptive and Cost-Aware Visual-Inertial Odometry](https://mlsanity.com/paper/2511.21083/) (2025)
- [Mono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping](https://mlsanity.com/paper/2605.17661/) (2026)

## Abstract (arXiv)

Visual-inertial odometry (VIO) has demonstrated remarkable success due to its low-cost and complementary sensors. However, existing VIO methods lack the generalization ability to adjust to different environments and sensor attributes. In this paper, we propose Adaptive VIO, a new monocular visual-inertial odometry that combines online continual learning with traditional nonlinear optimization. Adaptive VIO comprises two networks to predict visual correspondence and IMU bias. Unlike end-to-end approaches that use networks to fuse the features from two modalities (camera and IMU) and predict poses directly, we combine neural networks with visual-inertial bundle adjustment in our VIO system. The optimized estimates will be fed back to the visual and IMU bias networks, refining the networks in a self-supervised manner. Such a learning-optimization-combined framework and feedback mechanism enable the system to perform online continual learning. Experiments demonstrate that our Adaptive VIO manifests adaptive capability on EuRoC and TUM-VI datasets. The overall performance exceeds the currently known learning-based VIO methods and is comparable to the state-of-the-art optimization-based methods.