外观
论文arXiv 2609.071161 个月前
MAC-I$^2$：用于初始化与标定中鲁棒视觉-惯性融合的学习型度量感知协方差
原文链接：MAC-I$^2$: Learned Metrics-Aware Covariance for Robust Visual-Inertial Fusion in Initialization and Calibration（arXiv 2609.07116 · 发布日期：2026-09-07）
MAC-I$^2$: Learned Metrics-Aware Covariance for Robust Visual-Inertial Fusion in Initialization and Calibration
针对预设不确定性导致的VI融合鲁棒性不足，MAC-I$^2$学习视觉与IMU度量感知协方差，并在EuRoC和VBR初始化标定中验证。
核心要点
- 现有VI融合依赖预设不确定性，难以适应光照变化、动态物体和弱纹理场景。
- 方法将学习到的特征匹配不确定性传播为位姿协方差，并为IMU学习初始协方差。
- 通过保留训练子集微调，使IMU协方差在未见序列上保持度量感知。
- 在EuRoC成功率99.9%，重力和速度误差约降60%和42%；VBR成功率80%。
验证范围与局限：验证范围为EuRoC和VBR上的VI初始化与标定系统。
英文摘要 (Abstract)
Visual-Inertial (VI) fusion is fundamental to accurate and robust state estimation, where camera and IMU measurements are combined according to their respective uncertainties. Existing methods, however, fuse the two modalities with predefined uncertainties, regardless of how reliable each is in the local context, and thus often struggle under challenging environments involving illumination changes, dynamic objects, and textureless regions. In this paper, we present MAC-I$^2$, which achieves robust VI fusion through learned metric-aware covariance for both modalities, so that vision and IMU compete on their own merits rather than relying on predefined uncertainties. Here, metrics-aware means that each predicted covariance faithfully reflects the actual magnitude of the corresponding measurement noise. On the visual side, we propagate learned feature-matching uncertainties into pose covariances for the fusion. On the inertial side, motivated by the observation that integration error accumulates sharply at the early stage and grows slowly afterward, we design a learned IMU model with a learnable initial covariance, and propose a dedicated fine-tuning strategy on a held-out training subset to enable the metrics-aware covariance on unseen sequences. As a showcase, we build a VI initialization and calibration system, since accurate and robust initialization and calibration are the prerequisite for any reliable VI system. Experiments on EuRoC, and VBR show that MAC-I$^2$ substantially outperforms existing methods: it achieves a 99.9% initialization success rate on EuRoC, reducing gravity and velocity errors by about 60% and 42% over the strongest baseline, and maintains 80% success rate on challenging VBR sequences where baseline methods such as VINS-Mono drop below 10%.
作者：Xiang Fei, Yuheng Qiu, Can Xu, Yutian Chen, Ruogu Li, Xingxing Zuo, Wenshan Wang, Sebastian Scherer
来源
本页事实对应的公开页面，以原文为准。