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This paper introduces ASPIRE-VINS, a continuous-time visual-inertial navigation system that enhances trajectory estimation by integrating adaptive knot placement, multi-resolution splines, and robust 3D measurement residuals. The system addresses limitations of traditional keyframe-based methods by allowing for flexible evaluation of residuals at arbitrary timestamps and adapting to motion dynamics. Experimental results indicate that ASPIRE-VINS achieves competitive or reduced trajectory errors compared to existing baselines, showcasing its effectiveness in diverse motion and sensing scenarios.
ASPIRE-VINS reduces trajectory estimation errors by adapting knot placement and refining splines based on local motion variations.
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.
