MDE-VIO: Enhancing Visual-Inertial Odometry Using Learned Depth Priors
Integrating lightweight learned depth priors into VINS-Mono reduces ATE by up to 28.3%, improving robustness in low-texture environments.
Key Findings
Methodology
This work introduces a framework that embeds learned depth priors into the VINS-Mono backend, enforcing affine-invariant depth consistency and pairwise ordinal constraints. It employs a variance-based gating mechanism to filter unstable depth artifacts, utilizing models like VideoDepthAnything (VDA) and DepthAnythingAC (DAAC). The front-end enhancement uses Depth-Injected Feature Tracking (DIFT) by embedding normalized depth into the RGB channels, improving feature tracking in textureless regions. The backend optimization incorporates unary depth residuals and pairwise ordinal constraints into the factor graph, with dynamic weighting based on depth uncertainty. The system is optimized for real-time deployment on NVIDIA Jetson AGX Orin, balancing computational efficiency and accuracy.
Key Results
- On TartanGround and M3ED datasets, the proposed method reduces Absolute Trajectory Error (ATE) by up to 28.3%, outperforming baseline VINS-Mono especially in challenging low-texture and dynamic scenarios. The combination of depth residuals and ordinal constraints yields significant accuracy gains, with error reductions exceeding 20% in most sequences. The use of temporal uncertainty gating effectively filters noisy depth estimates, maintaining system stability. Video-based depth models like VDA demonstrate superior temporal stability over zero-shot models like DAAC, leading to more consistent trajectory estimates.
Significance
This research advances real-time monocular VIO by effectively integrating learned depth priors within computational constraints of edge devices. It addresses the critical challenge of low-texture environment robustness, a long-standing issue in visual odometry. The approach bridges the gap between high-accuracy dense depth estimation and resource-efficient optimization, enabling more reliable autonomous navigation in complex scenarios. The methodology’s ability to prevent divergence and maintain metric scale in real-world applications signifies a major step toward practical deployment of dense VIO systems on embedded platforms, impacting robotics, autonomous vehicles, and AR/VR industries.
Technical Contribution
The key technical innovations include the integration of lightweight, affine-invariant depth models into the VINS-Mono backend, the design of depth residuals (rD) that enforce scale consistency, and the introduction of pairwise ordinal constraints (rO) to preserve depth ordering. The system employs a variance-based gating mechanism to dynamically weight depth priors based on their temporal stability, filtering out unreliable estimates. Unlike end-to-end deep learning approaches, this framework maintains the optimization-based structure, ensuring robustness and generalization. The combination of geometric constraints and uncertainty-aware filtering provides a novel, efficient solution for resource-constrained environments, enabling real-time dense VIO with improved accuracy.
Novelty
This work is the first to incorporate affine-invariant learned depth predictions into the optimization backend of monocular VIO, using depth residuals and pairwise ordinal constraints simultaneously. It leverages temporal uncertainty estimation to filter unstable depth estimates, ensuring robustness. Unlike prior methods that rely solely on sparse features or heavy end-to-end models, this approach balances computational efficiency and geometric accuracy, making dense, metric-scale VIO feasible on edge devices. The combination of lightweight depth models, affine invariance, and uncertainty-guided filtering constitutes a significant innovation in the field.
Limitations
- 依赖预训练深度模型的性能，在极端光照或遮挡条件下可能出现误差，影响深度先验的可靠性。
- 在高速运动或严重遮挡场景中，深度残差和序次约束可能无法完全避免误差积累。
- 系统对深度模型的依赖增加了整体复杂度，未来需优化模型压缩与推理效率，以适应更广泛的硬件平台。
Future Work
未来将结合多模态信息（如激光雷达、多视角数据），提升深度估计的鲁棒性和精度。同时，优化深度模型的时序一致性，减少闪烁，提高在极端动态环境中的表现。还计划探索端到端训练与优化的结合策略，进一步提升系统的整体性能和适应性，推动Dense VIO在更复杂场景中的应用。
AI Executive Summary
视觉惯性里程计（VIO）在自主导航中扮演着重要角色，但在低纹理环境中表现不足，容易导致轨迹发散。传统方法主要依赖稀疏特征，难以应对纹理缺失的场景。近年来，深度学习带来了密集深度估计，尤其是基于Transformer的模型如VDA，提供了更丰富的几何信息，但其庞大的模型规模限制了在边缘设备上的实时应用。本文提出MDE-VIO，将轻量级深度先验融入VINS-Mono的优化后端，通过引入仿射不变深度一致性和成对序次约束，有效提升低纹理环境下的定位精度。系统在NVIDIA Jetson AGX Orin平台上实现，满足实时性要求。实验表明，该方法在TartanGround和M3ED数据集上，误差最高降低28.3%，优于传统VIO。深度残差和序次约束的结合增强了系统鲁棒性，避免在复杂场景中的发散问题。引入深度不确定性门控，有效过滤噪声，确保深度信息的可靠性。整体来看，该研究为边缘设备上的高精度VIO提供了新思路，推动自主导航技术的实用化。未来，将结合多模态信息，提升系统的适应性和鲁棒性，向更智能、更可靠的自主系统迈进。
Deep Analysis
Background
视觉惯性里程计（VIO）作为自主导航的核心技术，经历了从稀疏特征匹配到密集优化的演变。代表性工作如VINS-Mono、OKVIS采用非线性优化框架，有效融合IMU和视觉信息实现高精度轨迹估计。近年来，深度学习引入密集深度估计，极大丰富了几何先验，但模型庞大，难以在边缘设备实时部署。Transformer架构如ViT、DINO推动了密集深度的研究，带来更几何一致的深度预测，但计算成本高。传统方法在低纹理环境中表现不佳，亟需结合深度先验的轻量化方案。
Core Problem
现有VIO系统在低纹理环境中易发散，深度模型虽能提供丰富几何信息，但在边缘设备上难以实时运行，导致整体性能下降。深度尺度不一致和噪声问题严重影响优化效果。如何在保证实时性和精度的基础上，有效融合深度先验，成为关键难题。
Innovation
本文提出结合仿射不变深度预测与优化后端，设计深度残差和序次约束，利用动态不确定性门控过滤噪声，提升鲁棒性。创新点包括：引入轻量级深度模型（VDA、DAAC），满足边缘设备需求；设计尺度一致的深度残差（rD）；引入深度排序约束（rO）；采用不确定性门控机制过滤噪声。该方案在保证实时性的同时，大幅提升低纹理场景中的定位精度。
Methodology
- �� 以VINS-Mono为基础，增强前端特征追踪和后端优化能力。• 利用深度模型（VDA、DAAC）生成密集深度预测，作为几何先验。• 通过深度残差（rD）约束，确保尺度一致性，仿射参数（s,t）进行对齐。• 引入成对序次（rO）约束，保持深度排序稳定。• 在深度预测中引入不确定性（σ²），利用门控机制过滤不稳定估计。• 在优化中加入深度残差和序次约束，形成统一目标函数（J）。• 设计轻量级推理流程，确保在边缘设备实时运行。
Experiments
在TartanGround和M3ED数据集上验证，比较VINS-Mono、深度模型（VDA、DAAC）及不同融合策略。指标为绝对轨迹误差（ATE），多次重复确保统计显著性。调优参数（如门控阈值、权重）验证策略效果。进行消融实验，分析深度残差和序次约束贡献。系统在NVIDIA Jetson AGX Orin平台上实现，确保实时性。
Results
在TartanGround上，误差最高降低28.3%，深度残差和序次约束结合效果优越，提升鲁棒性。M3ED上误差提升超20%，在低纹理和快速运动场景表现尤佳。引入深度不确定性门控，有效过滤噪声，系统稳定性增强。视频深度模型VDA的时序稳定性优于零-shot模型DAAC，带来更优误差表现。整体验证了深度先验在边缘设备融合的可行性和优越性。
Applications
该技术适用于无人机、机器人自主导航、增强现实等场景，尤其在低纹理或复杂环境中表现出色。系统无需额外硬件，只需轻量深度模型和优化算法，便于部署在边缘设备。未来可结合多模态信息（如激光雷达）提升鲁棒性，推动自动驾驶、无人机巡航等行业应用。
Limitations & Outlook
依赖预训练深度模型性能，在极端光照或遮挡条件下可能失效。高速运动或遮挡严重场景中，深度残差和序次约束仍存在误差积累风险。模型复杂度和推理时间限制了更大规模应用，未来需优化模型压缩和推理效率。此外，深度模型的泛化能力仍需提升，以适应多样化环境。
Plain Language Accessible to non-experts
想象你在一个黑暗的房间里走路，没有明显的标志物，只能靠脚步声和手电筒的微弱光线判断位置。传统导航工具在这种环境中容易迷路，因为没有线索。现在，假设你带了一只会发光的手套，可以告诉你离墙壁的远近，甚至墙壁的相对高低。这个手套就像深度估计一样，提供了额外的空间信息，帮助你更准确地知道自己在哪里。本文的方法就像给导航系统装上了这种“发光手套”，让它在低纹理、复杂环境中也能稳稳地找到自己的位置。通过结合这种“光”，系统不仅更聪明，还更可靠，能在黑暗中安全前行。
ELI14 Explained like you're 14
想象你在一个黑暗的房间里走路，没有灯光，只能靠脚步声和手电筒微弱的光线判断自己位置。普通的导航工具在黑暗中很容易迷路，因为没有明显的线索。现在，假设你带了一只会发光的手套，可以告诉你离墙壁的距离，甚至墙壁的高低。这就像深度估计一样，给导航系统提供了额外的空间信息，让它知道自己离墙壁有多远，墙壁在什么位置。这样，即使在没有明显标志的环境中，系统也能准确找到自己的位置，不会迷路。本文的研究就像给导航系统装上了这种“发光手套”，让它在复杂、低纹理的环境中也能稳稳地走路，避免迷失方向。
Abstract
Traditional monocular Visual-Inertial Odometry (VIO) systems struggle in low-texture environments where sparse visual features are insufficient for accurate pose estimation. To address this, dense Monocular Depth Estimation (MDE) has been widely explored as a complementary information source. While recent Vision Transformer (ViT) based complex foundational models offer dense, geometrically consistent depth, their computational demands typically preclude them from real-time edge deployment. Our work bridges this gap by integrating learned depth priors directly into the VINS-Mono optimization backend. We propose a novel framework that enforces affine-invariant depth consistency and pairwise ordinal constraints, explicitly filtering unstable artifacts via variance-based gating. This approach strictly adheres to the computational limits of edge devices while robustly recovering metric scale. Extensive experiments on the TartanGround and M3ED datasets demonstrate that our method prevents divergence in challenging scenarios and delivers significant accuracy gains, reducing Absolute Trajectory Error (ATE) by up to 28.3%. Code will be made available.