Showing papers on "Invariant extended Kalman filter published in 2020"
TL;DR: This work develops a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design and demonstrates how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions.
Abstract: Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental an...
145 citations
TL;DR: This article provides a complete, self-contained, tutorial derivation of the relative Kalman filter, which has been thoroughly motivated but only briefly described to date, and derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics definedwith respect to the vehicle’s body frame.
Abstract: This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an iner...
41 citations
TL;DR: The proposed IEKF differs from existing techniques in literature on the account that it is derived using minimal tools from differential geometry that simplifies its representation and derivation to a large extent.
19 citations
13 Jan 2020
TL;DR: This letter presents a solution for the state estimation and control problems for a class of unconventional vertical takeoff and landing UAVs operating in forward-flight conditions using the Invariant Extended Kalman Filter (IEKF), which offers several advantages compared to standard multiplicative EKFs traditionally used in aerospace and robotics problems.
Abstract: This letter presents a solution for the state estimation and control problems for a class of unconventional vertical takeoff and landing (VTOL) UAVs operating in forward-flight conditions. A tightly-coupled state estimation approach is used to estimate the aircraft navigation states, sensor biases, and the wind velocity. State estimation is done within a matrix Lie group framework using the Invariant Extended Kalman Filter (IEKF), which offers several advantages compared to standard multiplicative EKFs traditionally used in aerospace and robotics problems. An $SO(3)$ -based attitude controller is employed, leading to a single attitude control law without a separate sideslip control loop. A control allocator is used to determine how to use multiple, possibly redundant, actuators to produce the desired control moments. The wind velocity estimates are used in the attitude controller and the control allocator to improve performance. A numerical example is considered using a sample VTOL tailsitter-type UAV with four control surfaces. Monte-Carlo simulations demonstrate robustness of the proposed control and estimation scheme to various initial conditions, noise levels, and flight trajectories.
17 citations
TL;DR: In this paper, the Equivariant Filter (EqF) is proposed to exploit the equivariance properties of the system embedding and can be applied to any kinematic system on a Lie group.
10 citations
10 citations
09 Nov 2020
TL;DR: A threshold extended Kalman filter algorithm based on the influence mechanism analysis of SOC estimation is proposed, which is employed to improve the robustness of EKF and the performance of SOC estimating has been improved significantly.
Abstract: Model-based algorithms are widely employed in state of charge estimation. However, due to the complex operation environment and rich noise in the measured battery voltage and current, it is difficult to estimate terminal voltage accurately based on the battery model, resulting in great estimation deviation of SOC further. To address the issue, a threshold extended Kalman filter algorithm based on the influence mechanism analysis of SOC estimation is proposed. In the proposed algorithm, the threshold for SOC, which is set by the residual non-Gauss voltage noise between the measured terminal voltage and the estimated terminal voltage, is employed to improve the robustness of EKF. Then, an experimental platform is built and two different EV operation conditions are selected to verify the algorithm on a Li-ion battery. Combining the comparison among EKF, adaptive Kalman filter and the proposed T-EKF, some important results are obtained for the proposed algorithms: (1) the maximum errors of SOC estimation under Urban Dynamometer Driving Schedule (UDDS) and urban bus operation condition (UBOC) are about 1.3%, 2.9%, respectively; (2) corresponding error ranges are [-0.8%, 1.33%] and [-2.96%, 0.1%], respectively. Compared to other algorithms, the performance of SOC estimation has been improved significantly.
6 citations
01 Jul 2020
TL;DR: This work proposes a first-order approximation of the noise covariance in the invariant frame of the Invariant Extended Kalman Filter (IEKF) and demonstrates that the first- order approximation improves the performance of the IEKF and yields superior transient performance over the standard EKF.
Abstract: We consider a nonlinear estimation problem where a unicycle vehicle moves with unknown disturbances generated from linear time-invariant systems. The vehicle measures its position to estimate its state and disturbance information simultaneously. We show that this system is invariant under the action of a Lie group and design an Invariant Extended Kalman Filter (IEKF). We propose a first-order approximation of the noise covariance in the invariant frame. Through Monte-Carlo simulations, we demonstrate that the first-order approximation improves the performance of the IEKF and that the IEKF yields superior transient performance over the standard EKF.
3 citations