Development of an error-state Kalman Filter for Emergency Maneuvering of Trucks

2020 ◽  
Author(s):  
Martin Wachsmuth ◽  
Axel Koppert ◽  
Li Zhang ◽  
Volker Schwieger
Keyword(s):  
Kalman Filter ◽  
Error State

Robust Error-State Kalman Filter for Estimating IMU Orientation

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Rachel V. Vitali ◽  
Ryan S. McGinnis ◽  
Noel C. Perkins
Keyword(s):  
Kalman Filter ◽  
Error State

A Collaborative Visual Localization Scheme for a Low-Cost Heterogeneous Robotic Team With Non-Overlapping Perspectives

2019 ◽  
Author(s):  
Benjamin Abruzzo ◽  
David Cappelleri ◽  
Philippos Mordohai
Keyword(s):  
Kalman Filter ◽  
Mobile Robots ◽  
Low Cost ◽  
Visual Localization ◽  
Goal Location ◽  
Relative Localization ◽  
Localization Scheme ◽  
Image Quantization ◽  
Aerial Robot ◽  
Error State

Abstract This paper presents and evaluates a relative localization scheme for a heterogeneous team of low-cost mobile robots. An error-state, complementary Kalman Filter was developed to fuse analytically-derived uncertainty of stereoscopic pose measurements of an aerial robot, made by a ground robot, with the inertial/visual proprioceptive measurements of both robots. Results show that the sources of error, image quantization, asynchronous sensors, and a non-stationary bias, were sufficiently modeled to estimate the pose of the aerial robot. In both simulation and experiments, we demonstrate the proposed methodology with a heterogeneous robot team, consisting of a UAV and a UGV tasked with collaboratively localizing themselves while avoiding obstacles in an unknown environment. The team is able to identify a goal location and obstacles in the environment and plan a path for the UGV to the goal location. The results demonstrate localization accuracies of 2cm to 4cm, on average, while the robots operate at a distance from each-other between 1m and 4m.

scholarly journals Improved Underwater Integrated Navigation System using Unscented Filtering Approach

2015 ◽  
Vol 69 (3) ◽  
pp. 561-581 ◽  
Author(s):  
Mohammad Shabani ◽  
Asghar Gholami
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Models ◽  
Error State ◽  
Filtering Approach

In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.

scholarly journals Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249577
Author(s):  
Michael V. Potter ◽  
Stephen M. Cain ◽  
Lauro V. Ojeda ◽  
Reed D. Gurchiek ◽  
Ryan S. McGinnis ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lower Limb ◽  
Injury Risk ◽  
Human Subjects ◽  
Human Movement ◽  
Lower Limbs ◽  
Direct Evaluation ◽  
Body Model ◽  
Optical Motion ◽  
Error State

Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.

Attitude Estimation By Separate-Bias Kalman Filter-Based Data Fusion

2004 ◽  
Vol 57 (2) ◽  
pp. 261-273 ◽  
Author(s):  
Peyman Setoodeh ◽  
Alireza Khayatian ◽  
Ebrahim Frajah
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Dynamic Modelling ◽  
Limited Range ◽  
Attitude Estimation ◽  
Fusion Algorithm ◽  
Experimental Implementation ◽  
Slow Drift ◽  
Error State ◽  
State Form

Attitude estimation systems often use two or more different sensors to increase reliability and accuracy. Although gyroscopes do not have problems like limited range, interference, and line of sight obscuration, they suffer from slow drift. On the other hand, inclinometers are drift-free but they are sensitive to transverse accelerations and have slow dynamics. This paper presents an extended Kalman filter (EKF)-based data fusion algorithm which utilizes the complementary noise profiles of these two types of sensors to extend their limits. To avoid complexities of dynamic modelling of the platform and its interaction with the environment, gyro modelling will be used to implement indirect (error state) form of the Kalman filter. The great advantage of this approach is its independence from the structure of the platform and its applicability to any system with a similar set of sensors. Separate bias formulation of the Kalman filter will be used to reduce the computational complexity of the algorithm. In addition, a systematic approach based on wavelet decomposition will be utilized to estimate noise covariances used in the Kalman filter formulation. This approach solves many of the convergence problems encountered in the implementation of EKF due to the choice of covariance matrices. Experimental implementation of the estimator shows the excellent performance of the filter.

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