AI 中文总结
ASPIRE-VINS是结合AKP、MRS与3D-MSR的连续时间VINS框架，可自适应轨迹节点分配，在多样运动与传感条件下实现更低或相当的轨迹误差，提升视觉-惯性导航的灵活性与精度。
AI 中文摘要
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。现代采用IMU预积分的离散时间方法精度高、效率强，但基于关键帧的表示在需在任意时间戳评估残差或需要与运动相关的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时间函数解决该问题，但均匀分布的节点可能无法充分表示快速动态或对静态区间过度参数化。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）与3D测量空间残差（3D-MSR）。AKP根据局部运动变化分配节点，MRS在切空间添加有界局部细化，3D-MSR通过将变换后的特征与3D测量空间中校准的观测射线对齐提供方位一致性。实验表明，ASPIRE-VINS达到与对比基准相当或更低的轨迹误差，证明在多样运动与传感条件下运动自适应连续时间轨迹建模的有效性。
英文摘要
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.
Comments8 pages, 5 figures. Accepted for publication in IEEE Robotics and Automation Letters (RA-L), June 2026
Journal refIEEE Robotics and Automation Letters, vol. 11, no. 9, pp. 10625-10632, 2026
