adaptive complementary filtering

This work addresses the challenge of attitude estimation when only scalar inertial measurements—such as partial vector observations along a single body-fixed axis—are available. The authors propose a novel complementary filter formulated directly on the SO(3) manifold, wherein the innovation term is specifically restructured to accommodate the scalar output structure. They establish almost global asymptotic stability of the attitude estimate under the condition that at least three inertial vectors are measured along the same body axis, and further derive sufficient conditions for convergence in two distinct dual-scalar measurement configurations. Numerical experiments demonstrate that the proposed method maintains robustness and effectiveness even under severe sensor constraints or with emerging scalar sensing modalities.

This work addresses the challenges of abrupt outliers, bias drift, and high computational load encountered when deploying large-scale, low-cost MEMS inertial sensor arrays for land navigation. To this end, the authors propose a robust extended Kalman filter (EKF) architecture, termed RISAF, which incorporates dynamic percentile gating and real-time bias tracking prior to the EKF prediction step. This approach effectively suppresses anomalous measurements and compensates for individual sensor drift without expanding the state dimensionality, enabling efficient fusion of data from hundreds of IMUs. Experimental results demonstrate that, in GNSS-denied environments, RISAF significantly improves heading accuracy and mitigates drift accumulation compared to simple averaging, achieving navigation performance approaching that of tactical-grade inertial systems.

本文设计并实现了一种结合卡尔曼滤波器的算法，用于解决低成本惯性传感器在倾斜角度估计中遇到的噪声和漂移问题，通过融合加速度计和陀螺仪数据提高估计精度。

Accurate 3D rigid-body pose estimation under rich tactile contact remains challenging, particularly due to the non-commutativity of SO(3) rotations, which undermines convergence stability and orientation accuracy of conventional filtering approaches (e.g., Euler angles, quaternions) during sustained contact. Method: This work introduces the first tactile-force-and-torque-aided complementary filter operating directly on the SO(3) manifold, integrating superquadric geometric priors and Lie-group symmetry constraints. Contribution/Results: The proposed haptically driven SO(3) complementary filter achieves almost-global asymptotic stability, markedly improving orientation robustness and estimation accuracy during contact. Experiments on a dual-arm robotic platform demonstrate a 37% reduction in orientation error compared to state-of-the-art filters, with stable convergence under strong disturbances and multi-point sustained contact. This establishes a verifiable, manifold-aware paradigm for tactile–visual synergistic 3D manipulation.

To address low attitude estimation accuracy, high computational cost, and dynamically varying sensor reliability in visual-inertial odometry (VIO) for unmanned aerial vehicles operating in complex environments, this paper proposes a loosely coupled hybrid filtering framework. The method integrates an error-state Kalman filter (ESKF), a scaled unscented Kalman filter (UKF), IMU preintegration, and robust visual feature tracking. Its core innovations include: (1) a quaternion-focused hybrid error-state EKF/UKF architecture; and (2) a dynamic confidence mechanism leveraging multi-dimensional features—including image entropy and motion blur—to adaptively tune observation noise covariance. Evaluated on the EuRoC dataset, the approach achieves 49% and 57% improvements in position and attitude accuracy over standard ESKF, respectively, attaining accuracy comparable to a full UKF implementation while reducing computational overhead by approximately 48%. This yields significant gains in robustness, accuracy, and real-time performance under challenging conditions.

研究通过Allan方差校准法在卡尔曼滤波框架下设置IMU参数，解决了复杂工作条件下IMU参数难以有效调整的问题。

This work addresses the insufficient accuracy and efficiency of attitude estimation in foot-mounted AHRS-based pedestrian dead reckoning by proposing an adaptive complementary filtering method based on quaternion averaging. The approach fuses angular velocity, acceleration, and magnetic field measurements, employing Markley’s quaternion averaging instead of linear interpolation to achieve a more rigorous attitude fusion. Furthermore, it dynamically adjusts sensor weights according to gait phase detection and magnetic disturbance assessment. Compared to existing algorithms, the proposed method significantly reduces the root-mean-square error of attitude estimation while maintaining lower computational overhead than Kalman filters, thereby achieving an effective balance between precision and real-time performance.

研究通过在伽利略群上使用滑动窗口滤波器，联合估计未知延迟和导航状态，以提高存在测量延迟时的惯性导航精度。

This work addresses the degraded state estimation accuracy of legged robots under diverse gaits and environments caused by fixed noise parameters in invariant extended Kalman filters (InEKF). To overcome this limitation, we propose an online adaptive strategy that dynamically tunes the observation noise covariance based on filter residuals and innovations. Relying solely on IMU and leg kinematics—without requiring foot contact force sensors or manual parameter tuning—the method significantly enhances estimation robustness and accuracy. Experimental validation on a Unitree Go2 quadruped robot in both indoor and outdoor settings demonstrates a 25% reduction in position estimation error during trotting compared to the standard InEKF with fixed parameters, achieving performance comparable to approaches that leverage foot force measurements.

This work addresses the challenge of attitude estimation under GNSS-denied and highly dynamic flight conditions, where inertial measurement unit (IMU)-only approaches suffer from ambiguity between gravitational and inertial accelerations. To overcome this limitation, the paper proposes a nonlinear attitude estimation algorithm that fuses barometric altitude measurements. Two novel observers on SO(3) are developed: the first employs a cascaded structure combining a Riccati observer with complementary filtering to achieve almost global asymptotic stability, while the second constructs a unified observer on SO(3)×ℝ² that relaxes observability requirements and ensures local exponential stability. Notably, the method eliminates the need for conventional velocity sensors by leveraging barometric data to enhance vertical motion awareness. Evaluation on both simulated and real flight data demonstrates that the proposed approach delivers lightweight, reliable, and efficient attitude estimation performance.