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This paper introduces CT-VIR, a continuous-time visual-inertial-ranging fusion method for indoor localization that addresses the challenges of sparse and unreliable ranging data. The approach uses VIO motion priors and UWB ranging to create virtual anchors and filter outliers, improving range reliability and mitigating geometric degeneration. By parameterizing the pose trajectory with a B-spline and formulating constraints within a sliding-window graph, CT-VIR achieves accurate and consistent localization, even with asynchronous multi-sensor sampling.
Continuous-time trajectory estimation with spline parameterization unlocks robust visual-inertial odometry, even with sparse and noisy ranging data.
Visual-inertial odometry (VIO) is widely used for mobile robot localization, but its long-term accuracy degrades without global constraints. Incorporating ranging sensors such as ultra-wideband (UWB) can mitigate drift; however, high-accuracy ranging usually requires well-deployed anchors, which is difficult to ensure in narrow or low-power environments. Moreover, most existing visual-inertial-ranging (VIR) fusion methods rely on discrete time-based filtering or optimization, making it difficult to balance positioning accuracy, trajectory consistency, and fusion efficiency under asynchronous multi-sensor sampling. To address these issues, we propose a spline-based continuous-time state estimation method for VIR fusion localization. In the preprocessing stage, VIO motion priors and UWB ranging measurements are used to construct virtual anchors and reject outliers, thereby alleviating geometric degeneration and improving range reliability. In the estimation stage, the pose trajectory is parameterized in continuous time using a B-spline, while inertial, visual, and ranging constraints are formulated as factors in a sliding-window graph. The spline control points, together with a small set of auxiliary parameters, are then jointly optimized to obtain a continuous-time trajectory estimate. Evaluations on public datasets and real-world experiments demonstrate the effectiveness and practical potential of the proposed approach.
