MDE-VIO: Enhancing Visual-Inertial Odometry Using Learned Depth Priors

2026-02-13 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

MDE-VIO is a novel framework that enhances monocular Visual-Inertial Odometry (VIO) by integrating learned depth priors into the VINS-Mono optimization backend, specifically for real-time edge device deployment. It addresses the limitations of traditional VIO in low-texture environments by enforcing affine-invariant depth consistency and pairwise ordinal constraints, while filtering unstable artifacts using variance-based gating. The system was evaluated on the TartanGround and M3ED datasets, demonstrating significant accuracy gains, reducing Absolute Trajectory Error (ATE) by up to 28.3% and preventing divergence in challenging scenarios. This approach maintains computational efficiency suitable for devices like the NVIDIA Jetson AGX Orin, achieving 12ms latency (83 FPS) with DepthAnythingAC and 44ms latency (23 FPS) with VideoDepthAnything.

Key takeaway

For Computer Vision Engineers developing real-time VIO systems for edge devices, prioritizing temporally consistent depth priors and integrating them into the optimization backend is crucial. Your choice of Monocular Depth Estimation (MDE) model should favor video-based approaches like VideoDepthAnything over zero-shot models to avoid inter-frame flicker, which can destabilize trajectory estimation. This strategy will enhance localization accuracy and prevent system divergence in challenging, low-texture environments, improving overall system robustness.

Key insights

Integrating temporally consistent learned depth priors into VIO backend optimization significantly improves accuracy and robustness on edge devices.

Principles

• Temporal consistency is paramount for depth priors in VIO.
• Backend optimization generally outperforms frontend depth injection.
• Geometric priors prevent catastrophic VIO failure in challenging scenes.

Method

MDE-VIO integrates depth priors into VINS-Mono via Depth-Injected Feature Tracking (DIFT) and backend constraints, using variance-gated affine and pairwise ordinal residuals, and an uncertainty-guided dynamic adaptation for weighting.

In practice

• Use video-based MDE models for VIO to ensure temporal stability.
• Prioritize backend integration of depth priors over frontend injection.
• Implement uncertainty-based weighting to filter unstable depth estimates.

Topics

• Visual-Inertial Odometry
• Monocular Depth Estimation
• Edge AI
• Factor Graph Optimization
• Depth Priors

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Researcher, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.