自适应样条视觉惯性导航系统，提升复杂运动下的定位精度
ASPIRE-VINS: Adaptive Spline-based Visual-inertial Navigation System With Robust 3D Measurement Residuals
- 用自适应节点分布动态调整轨迹拟合密度
- 在多种运动条件下实现更低的轨迹误差
- 适合需要高精度、实时响应的自动驾驶场景
视觉惯性导航系统通过融合视觉与惯性数据估计六自由度运动。现代离散时间方法结合惯性测量单元预积分，具有高精度和高效率，但关键帧表示在需任意时间戳评估残差或运动相关时间分辨率时灵活性不足。连续时间样条通过将轨迹表示为平滑的时间函数解决此问题，但均匀分布的节点可能无法充分表征快速动态或过度参数化静止区间。本文提出ASPIRE-VINS，一种连续时间VINS框架，结合自适应节点放置（AKP）、多分辨率样条（MRS）和3D测量空间残差（3D-MSR）。AKP根据局部运动变化分配节点，MRS在切空间中添加有界局部细化，3D-MSR通过将变换特征与校准观测射线在3D测量空间对齐实现方向一致性。实验表明，ASPIRE-VINS在对比基线中达到相当或更低的轨迹误差，验证了在多样运动和传感条件下运动自适应连续时间轨迹建模的有效性。
原文摘要 · Abstract (English)
Visual-inertial navigation systems estimate six-degree-of-freedom motion by fusing visual and inertial data. Modern discrete-time methods with IMU preintegration provide strong accuracy and efficiency, but keyframe-based representations can be less flexible when residuals must be evaluated at arbitrary timestamps or when motion-dependent temporal resolution is needed. Continuous-time splines address this issue by representing the trajectory as a smooth temporal function, but uniformly spaced knots can under-represent rapid dynamics or over-parameterize static intervals. This letter proposes ASPIRE-VINS, a continuous-time VINS framework that combines adaptive knot placement (AKP), multi-resolution splines (MRS), and 3D measurement-space residuals (3D-MSR). AKP allocates knots according to local motion variation, MRS adds bounded local refinement in tangent space, and 3D-MSR provides bearing consistency by aligning transformed features with calibrated observation rays in 3D measurement space. Experiments show that ASPIRE-VINS achieves competitive or lower trajectory errors than the compared baselines, demonstrating the effectiveness of motion-adaptive continuous-time trajectory modeling under diverse motion and sensing conditions.
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