arXiv 2403.072252024-03-12cs.RO

Stereo-NEC：利用法线对极约束增强双目视觉惯性SLAM初始化

Stereo-NEC: Enhancing Stereo Visual-Inertial SLAM Initialization with Normal Epipolar Constraints

Stevens Institute of Technology（史蒂文斯理工学院）；OPPO US Research Center（OPPO美国研究中心）；Stony Brook University（石溪大学）

Weihan Wang, Chieh Chou, Ganesh Sevagamoorthy, Kevin Chen, Zheng Chen, Ziyue Feng, Youjie Xia, Feiyang Cai, Yi Xu, Philippos Mordohai

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AI总结：

本研究提出Stereo-NEC双目视觉惯性SLAM初始化方法，通过独立估计陀螺仪零偏并优化、更新旋转估计、3自由度BA提升平移精度，结合法线对极约束残差判定初始化成功与否，在EuRoC数据集上精度鲁棒性优于ORB-SLAM3且计算速度具竞争力。

摘要：

我们提出了一种适用于双目视觉惯性SLAM系统的高精度、鲁棒初始化方法。现有最先进方法严重依赖纯视觉SLAM系统的精度来估计惯性变量，且不会更新相机位姿，这可能会损害精度与鲁棒性，我们的方法则提供了不同的解决方案。我们认识到精确的陀螺仪零偏估计对旋转精度有关键影响，而旋转精度又会因平移误差的累积进而影响轨迹精度。为解决该问题，我们首先独立估计陀螺仪零偏，并基于此构建最大后验概率问题以进行进一步优化。优化完成后，我们通过在陀螺仪测量值中去除陀螺仪零偏后执行IMU积分，来更新旋转估计结果。随后我们利用鲁棒且精确的旋转估计结果，通过3自由度光束法平差提升平移估计精度。此外，我们提出了一种通过评估法线对极约束残差来判定初始化是否成功的新方法。在EuRoC数据集上的大量评估表明，我们的方法在精度与鲁棒性上表现优异。其在绝对轨迹误差和相对旋转误差方面优于当前领先的双目视觉惯性初始化方法ORB-SLAM3，同时保持了具备竞争力的计算速度。值得注意的是，即便仅使用5个关键帧进行初始化，我们的方法在旋转精度上也始终优于使用10个关键帧的现有最先进方法。

英文摘要：

We propose an accurate and robust initialization approach for stereo visual-inertial SLAM systems. Unlike the current state-of-the-art method, which heavily relies on the accuracy of a pure visual SLAM system to estimate inertial variables without updating camera poses, potentially compromising accuracy and robustness, our approach offers a different solution. We realize the crucial impact of precise gyroscope bias estimation on rotation accuracy. This, in turn, affects trajectory accuracy due to the accumulation of translation errors. To address this, we first independently estimate the gyroscope bias and use it to formulate a maximum a posteriori problem for further refinement. After this refinement, we proceed to update the rotation estimation by performing IMU integration with gyroscope bias removed from gyroscope measurements. We then leverage robust and accurate rotation estimates to enhance translation estimation via 3-DoF bundle adjustment. Moreover, we introduce a novel approach for determining the success of the initialization by evaluating the residual of the normal epipolar constraint. Extensive evaluations on the EuRoC dataset illustrate that our method excels in accuracy and robustness. It outperforms ORB-SLAM3, the current leading stereo visual-inertial initialization method, in terms of absolute trajectory error and relative rotation error, while maintaining competitive computational speed. Notably, even with 5 keyframes for initialization, our method consistently surpasses the state-of-the-art approach using 10 keyframes in rotation accuracy.

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