scholarly journals A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

2021 ◽  
pp. 014233122110114
Author(s):  
Mostafa Osman ◽  
Mohamed W Mehrez ◽  
Mohamed A Daoud ◽  
Ahmed Hussein ◽  
Soo Jeon ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Optimization Problems ◽  
Unscented Kalman Filter ◽  
Computational Cost ◽  
Intelligent Vehicles ◽  
Superior Performance ◽  
Moving Horizon Estimation ◽  
Missing Measurements ◽  
Fusion Scheme

In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

Nonlinear Kalman Filtering With Expensive Forward Models Via Measure Change

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Brian J. Burrows ◽  
Douglas Allaire
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Computational Cost ◽  
Test Problems ◽  
Model Approximation ◽  
Forward Models ◽  
Estimation Scheme ◽  
Increase In Accuracy

Abstract Filtering is a subset of a more general probabilistic estimation scheme for estimating the unobserved parameters from the observed measurements. For nonlinear, high speed applications, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are common estimators; however, expensive and strongly nonlinear forward models remain a challenge. In this paper, a novel Kalman filtering algorithm for nonlinear systems is developed, where the numerical approximation is achieved via a change of measure. The accuracy is identical in the linear case and superior in two nonlinear test problems: a challenging 1D benchmarking problem and a 4D structural health monitoring problem. This increase in accuracy is achieved without the need for tuning parameters, rather relying on a more complete approximation of the underlying distributions than the Unscented Transform. In addition, when expensive forward models are used, we achieve a significant reduction in computational cost without resorting to model approximation.

Model-Based Control of Electroslag Remelting Process Using Unscented Kalman Filter

2008 ◽  
Author(s):  
Seokyoung Ahn ◽  
Joseph J. Beaman ◽  
Rodney L. Williamson ◽  
David K. Melgaard
Keyword(s):  
Kalman Filter ◽  
Temperature Distribution ◽  
Controller Design ◽  
Unscented Kalman Filter ◽  
Liquid Pool ◽  
Electroslag Remelting ◽  
Superior Performance ◽  
Process Conditions ◽  
Reduced Order ◽  
Highly Nonlinear

Electroslag Remelting (ESR) is used widely throughout the specialty metals industry. The process generally consists of a regularly shaped electrode that is immersed a small amount in liquid slag at a temperature higher than the melting temperature of the electrode. Melting droplets from the electrode fall through the lower density slag into a liquid pool constrained by a crucible and solidify into an ingot. High quality ingots require that electrode melt rate and immersion depth be controlled. This can be difficult when process conditions are such that the temperature distribution in the electrode is not at steady state. A new method of ESR control has been developed that incorporates an accurate, reduced-order melting model to continually estimate the temperature distribution in the electrode. The ESR process is highly nonlinear, noisy, and has coupled dynamics. An extended Kalman filter and an unscented Kalman filter were chosen as possible estimators and compared in the controller design. During the highly transient periods in melting, the unscented Kalman filter showed superior performance for estimating and controlling the system.

scholarly journals Moving Horizon Estimation for GNSS-IMU sensor fusion

2020 ◽  
pp. 112-122
Author(s):  
Guido Sánchez ◽  
Marina Murillo ◽  
Lucas Genzelis ◽  
Nahuel Deniz ◽  
Leonardo Giovanini
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Satellite System ◽  
Measurement Unit ◽  
Moving Horizon Estimation ◽  
Ground Vehicle ◽  
Global Navigation Satellite ◽  
Time Systems ◽  
True Values

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

scholarly journals Train Localization using Unscented Kalman Filter – Based Sensor Fusion

2018 ◽  
Vol 1 (2) ◽  
pp. 35-41
Author(s):  
I Faruqi ◽  
M. B. Waluya ◽  
Y. Y. Nazaruddin ◽  
T. A. Tamba ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Precision Measurement ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Experimental Results ◽  
Fusion Method ◽  
Localization Problem ◽  
Rail Systems ◽  
Fusion Methods

This paper presents an application of sensor fusion methods based on Unscented Kalman filter (UKF) technique for solving train localization problem in rail systems. The paper first reports the development of a laboratory-scale rail system simulator which is equipped with various onboard and wayside sensors that are used to detect and locate the train vehicle movements in the rail track. Due to the low precision measurement data obtained by each individual sensor, a sensor fusion method based on the UKF technique is implemented to fuse the measurement data from several sensors. Experimental results which demonstrate the effectiveness of the proposed UKF-based sensor fusion method for solving the train localization problem is also reported.

A Comparison of SLAM Prediction Densities Using the Kolmogorov Smirnov Statistic

2016 ◽  
Vol 04 (04) ◽  
pp. 245-254
Author(s):  
Akshay Rao ◽  
Wang Han ◽  
P. G. C. N. Senarathne
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Fusion Algorithm ◽  
Central Difference ◽  
Localization Algorithms ◽  
Kolmogorov Smirnov ◽  
Prediction Density ◽  
Smirnov Statistic

Accurate pose and trajectory estimates, are necessary components of autonomous robot navigation system. A wide variety of Simultaneous Localization and Mapping (SLAM) and localization algorithms have been developed by the robotics community to cater to this requirement. Some of the sensor fusion algorithms employed by SLAM and localization algorithms include the particle filter, Gaussian Particle Filter, the Extended Kalman Filter, the Unscented Kalman Filter, and the Central Difference Kalman Filter. To guarantee a rapid convergence of the state estimate to the ground truth, the prediction density of the sensor fusion algorithm must be as close to the true vehicle prediction density as possible. This paper presents a Kolmogorov–Smirnov statistic-based method to compare the prediction densities of the algorithms listed above. The algorithms are compared using simulations of noisy inputs provided to an autonomous robotic vehicle, and the obtained results are analyzed. The results are then validated using data obtained from a robot moving in controlled trajectories similar to the simulations.

Comparison of Unscented and Extended Kalman Filters with Application in Vehicle Navigation

2016 ◽  
Vol 70 (2) ◽  
pp. 411-431 ◽  
Author(s):  
Cheng Yang ◽  
Wenzhong Shi ◽  
Wu Chen
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Field Tests ◽  
Superior Performance ◽  
Vehicle Navigation ◽  
Prototype Model ◽  
Computationally Efficient ◽  
Theoretical Formulation ◽  
Nonlinear State Estimation

The Unscented Kalman Filter (UKF) is a well-known nonlinear state estimation method. It shows superior performance at nonlinear estimation compared to the Extended Kalman Filter (EKF). This paper is devoted to an investigation between UKF and EKF with different feedback control modes in vehicle navigation. Theoretical formulation, simulation and field tests have been carried out to compare the performance of UKF and EKF. The simulation and test results demonstrate that the estimated state of a UKF relies on the measurements and is less sensitive to historical model information. The results also indicate that UKF has benefits for prototype model design due to avoidance of calculation of a Jacobian matrix. EKF, however, is more computationally efficient and more stable.

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