Adaptive Invariant Extended Kalman Filter with Noise Covariance Tuning for Attitude Estimation
Attitude estimation is crucial in aerospace engineering, robotics, and virtual reality applications, but faces difficulties due to nonlinear system dynamics and sensor limitations. This paper addresses the challenge of attitude estimation using quaternion-based adaptive right invariant extended Kalman filtering (RI-EKF) that integrates data from inertial and magnetometer sensors. Our approach applies the expectation-maximization (EM) algorithm to estimate noise covariance, exploiting RI-EKF symmetry properties. We analyze the adaptive RI-EKF's stability, convergence, and accuracy, validating its performance through simulations and comparison with the left invariant EKF. Monte Carlo simulations validate the effectiveness of our noise covariance estimation technique across various window lengths.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Neural Aided Adaptive Innovation-Based Invariant Kalman Filter
Autonomous platforms require accurate positioning to complete their tasks. To this end, a Kalman filter-based algorithms, such as the extended Kalman filter or invariant Kalman filter, utilizing inertial and external sen…
Domain AdaptationNoise EstimationIterated Invariant Extended Kalman Filter (IterIEKF)
We study the mathematical properties of the Invariant Extended Kalman Filter (IEKF) when iterating on the measurement update step, following the principles of the well-known Iterated Extended Kalman Filter. This iterativ…
BenchmarkingAdaptive Invariant Extended Kalman Filter for Legged Robot State Estimation
State estimation is crucial for legged robots as it directly affects control performance and locomotion stability. In this paper, we propose an Adaptive Invariant Extended Kalman Filter to improve proprioceptive state es…
Contact DetectionInvariant Kalman Filtering with Noise-Free Pseudo-Measurements
In this paper, we focus on developing an Invariant Extended Kalman Filter (IEKF) for extended pose estimation for a noisy system with state equality constraints. We treat those constraints as noise-free pseudo-measuremen…
Pose EstimationIterated Invariant EKF for Quadruped Robot Odometry
Kalman filter-based algorithms are fundamental for mobile robots, as they provide a computationally efficient solution to the challenging problem of state estimation. However, they rely on two main assumptions that are d…