通过运动特性提升单目视觉惯性里程计的尺度精度
Motion as a Sensing Modality for Metric Scale in Monocular Visual-Inertial Odometry
- 利用运动轨迹中的加速度差异，建立尺度与惯性状态的耦合关系
- 转弯和环形路径使尺度误差降至4.8%，直线运动误差达9.2%
- 提出可实时计算的激励度指标，指导轨迹设计
单目视觉惯性里程计（VIO）无法仅靠视觉恢复度量尺度，需依赖惯性测量。本文通过轨迹相关的可观测性分析发现，由曲线运动产生的平动加速度是将尺度与惯性状态耦合的根本来源，而非匀速直线运动。该关系基于惯性测量单元（IMU）模型中的重力-加速度不对称性，推导出可观测性矩阵的秩条件，并提出一种可从原始IMU数据计算的轻量级激励度指标。在配备单目相机和消费级IMU的差速驱动机器人上进行的受控实验验证了理论：直线运动尺度误差为9.2%，圆周运动为6.4%，八字形轨迹为4.8%，激励度跨度达四个数量级。结果表明，轨迹设计是提升度量尺度恢复精度的有效手段。
原文摘要 · Abstract (English)
Monocular visual-inertial odometry (VIO) cannot recover metric scale from vision alone; scale must be resolved through inertial measurements. We present a trajectory-dependent observability analysis showing that translational acceleration, produced by curvature, not constant-speed straight-line travel, is the fundamental source that couples scale to the inertial state. This relationship is formalized through the gravity-acceleration asymmetry in the IMU model, from which we derive rank conditions on the observability matrix and propose a lightweight excitation metric computable from raw IMU data. Controlled experiments on a differential-drive robot with a monocular camera and consumer-grade IMU validate the theory, with straight-line motion yielding 9.2% scale error, circular motion 6.4%, and figure-eight motion 4.8%, with excitation spanning four orders of magnitude. These results establish trajectory design as a practical mechanism for improving metric scale recovery.
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