提出新方法提升视觉惯性里程计初始化精度，尤其适合快速运动和退化场景。
A Robust and Efficient Visual-Inertial Initialization with Probabilistic Normal Epipolar Constraint
- 基于概率法向对极约束优化陀螺仪偏差估计
- 融合视觉与惯性数据高效求解速度、重力与尺度参数
- 新增精修模块显著降低重力与尺度误差，适合高动态场景
准确可靠的初始化对视觉惯性里程计（VIO）至关重要，劣质初始化会严重降低位姿估计精度。初始化阶段需估计加速度计偏置、陀螺仪偏置、初始速度、重力等参数。现有多数方法采用结构光流（SfM）求解陀螺仪偏置，但在快速运动或退化场景下稳定性与效率不足。为此，本文在旋转-平移解耦框架基础上引入新不确定性参数与优化模块：首先，设计融合概率法向对极约束的陀螺仪偏置估计算法；其次，融合惯性与视觉测量高效求解速度、重力与尺度；最后，引入额外精修模块有效降低重力与尺度误差。在EuRoC数据集上的大量实验表明，本方法平均降低陀螺仪偏置误差16%、旋转误差4%，重力误差29%；在TUM数据集上，重力与尺度误差分别平均降低14.2%与5.7%。源码已公开于https://github.com/MUCS714/DRT-PNEC.git。
原文摘要 · Abstract (English)
Accurate and robust initialization is essential for Visual-Inertial Odometry (VIO), as poor initialization can severely degrade pose accuracy. During initialization, it is crucial to estimate parameters such as accelerometer bias, gyroscope bias, initial velocity, gravity, etc. Most existing VIO initialization methods adopt Structure from Motion (SfM) to solve for gyroscope bias. However, SfM is not stable and efficient enough in fast-motion or degenerate scenes. To overcome these limitations, we extended the rotation-translation-decoupled framework by adding new uncertainty parameters and optimization modules. First, we adopt a gyroscope bias estimator that incorporates probabilistic normal epipolar constraints. Second, we fuse IMU and visual measurements to solve for velocity, gravity, and scale efficiently. Finally, we design an additional refinement module that effectively reduces gravity and scale errors. Extensive EuRoC dataset tests show that our method reduces gyroscope bias and rotation errors by 16\% and 4\% on average, and gravity error by 29\% on average. On the TUM dataset, our method reduces the gravity error and scale error by 14.2\% and 5.7\% on average respectively. The source code is available at https://github.com/MUCS714/DRT-PNEC.git
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