Extended Kalman filter based localization for a mobile robot team

Lu, Jieying; Li, Chuxi; Su, Weizhou

doi:10.1109/ccdc.2016.7531118

Cited by 10 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The smaller the value of RMSE, the better the algorithm is. The calculation of the RMSE is shown as Equation (5). The RMSE est means the RMSE is calculated based on the filter's estimation and the RMSE noise is calculated based on the noisy position without filtering.…”

Section: The Results Of Discriminative Parameter Training Of the Paukfmentioning

confidence: 99%

“…As illustrated in Section 3, the parameters used in the trained variance PAUKF are the same as manually tuned PAUKF, meaning the only variable making the result change is the value of the measurement variance. The RMSE which is calculated based on the second row of Equation (5) shows that both the manually tuned PAUKF and variance-trained PAUKF are tested under the same noisy environment, and the RMSE of the manually tuned PF and trained PF shows the same value. This means the training process does not affect the performance of the PF.…”

Section: The Results Of Discriminative Parameter Training Of the Paukfmentioning

confidence: 99%

“…There are different kinds of applications of the Kalman filter family under investigation [1][2][3][4][5][6][7][8][9]. Most researchers involved defined the variance of prediction and measurement empirically, meaning not only that a lot of time is needed to tune the variance parameters, but also that it is unknown if the result generated is optimal for a given situation or not.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discriminative Parameter Training of the Extended Particle-Aided Unscented Kalman Filter for Vehicle Localization

Lin

Kim

2020

Applied Sciences

View full text Add to dashboard Cite

Location is one of the most important parameters of a self-driving car. To filter the sensor noise, we proposed the extended particle-aided unscented Kalman filter (PAUKF). Although the performance of the PAUKF improved, it still needed parameter tuning as other Kalman filter applications do. The characteristic of noise is important to the filter’s performance; the most important parameters therefore are the variances of the measurement. In most Kalman filter research, the variance of the filter is tuned manually, costing researchers plenty of time and yielding non-optimized results in most applications. In this paper, we propose a method that improves the performance of the extended PAUKF based on the coordinate descent algorithm by learning the most appropriate measurement variances. The results show that the performance of the extended PAUKF improved compared to the manually tuned extended PAUKF. By using the proposed training algorithm, practicability, training time efficiency and the estimation precision of the PAUKF improved compared to previous research.

show abstract

Section: The Results Of Discriminative Parameter Training Of the Paukfmentioning

confidence: 99%

Section: The Results Of Discriminative Parameter Training Of the Paukfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discriminative Parameter Training of the Extended Particle-Aided Unscented Kalman Filter for Vehicle Localization

Lin

Kim

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In [7], two distributed EKF algorithms are proposed for position estimation in a team of wheeled mobile robots (WMRs). Similarly, Lu et al [36] proposed a cooperative localization among a team of three WMRs using EKF. In [35], a network of multiple quadrotors is used to track a moving target.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Cooperative Localization Using Relative Position Estimation for Networked Systems With Minimum Number of Communication Links

Safaei

Mahyuddin

2019

IEEE Access

View full text Add to dashboard Cite

In this paper, a novel solution for cooperative localization problem involving a network with multiple mobile agents accessing to only one beacon agent is presented. The solution can be applied to a network with a minimum number of communication links (subjecting the network topology to be an undirected spanning tree). The objective is to provide each agent in the network with information on its absolute position without using any on-board global positioning sensors such as GPS receivers. The proposed solution uses an adaptive relative position estimation algorithm for each pair of neighboring mobile agents. The proposed estimation algorithm requires each agent to measure the relative inter-agent distance. The local velocity vector is also measured and transmitted to the neighboring agents. The estimation mechanism incorporating a signum function outperforms the recently established relative position estimation algorithms, in terms of positioning and tracking errors. An adaptive cooperative localization (ACL) algorithm is formed by augmenting the relative position estimation in a cooperative observer scheme suitably applicable for accomplishing localization task involving a network of mobile agents. The salient feature of the proposed ACL algorithm is that the communication graph among the agents needs only to have one undirected path between two agents in the network. Such convenience promotes easy practical implementation and lite computation for each agent. The proof of the proposed algorithm is provided using the Lyapunov stability theorem. Three simulation case studies are presented to evaluate the performance of the solution in different scenarios, including the stationary and moving beacon agent as well as the non-cooperatively controlled and cooperatively controlled network of mobile agents. The comparative studies reveal that the ACL algorithm is superior to the recently investigated linear-convex algorithm. The number of communication links required for the localization task to be carried out by the proposed algorithm is minimum, thus promoting a more preferable energy-efficient solution.

show abstract

“…Another integrated navigation system for UAV localization using the method of nonlinear smooth variable structure filter is presented and compared with an Extended Kalman Filter in [ 32 ]. Moreover, in [ 27 , 33 ] the authors evaluated the Extended Kalman Filter for inertial data fusion-based navigation localization for mobile robots. EKF has been most widely used for the application of multi-sensor fusion, either in a self-sufficient, or in a multiple model manner.…”

Section: Introductionmentioning

confidence: 99%

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

Elzoghby

Arafa³

et al. 2017

Sensors

View full text Add to dashboard Cite

Information fusion from multiple sensors ensures the accuracy and robustness of a navigation system, especially in the absence of global positioning system (GPS) data which gets degraded in many cases. A way to deal with multi-mode estimation for a small fixed wing unmanned aerial vehicle (UAV) localization framework is proposed, which depends on utilizing a Luenberger observer-based linear matrix inequality (LMI) approach. The proposed estimation technique relies on the interaction between multiple measurement modes and a continuous observer. The state estimation is performed in a switching environment between multiple active sensors to exploit the available information as much as possible, especially in GPS-denied environments. Luenberger observer-based projection is implemented as a continuous observer to optimize the estimation performance. The observer gain might be chosen by solving a Lyapunov equation by means of a LMI algorithm. Convergence is achieved by utilizing the linear matrix inequality (LMI), based on Lyapunov stability which keeps the dynamic estimation error bounded by selecting the observer gain matrix (L). Simulation results are presented for a small UAV fixed wing localization problem. The results obtained using the proposed approach are compared with a single mode Extended Kalman Filter (EKF). Simulation results are presented to demonstrate the viability of the proposed strategy.

show abstract

Extended Kalman filter based localization for a mobile robot team

Cited by 10 publications

References 15 publications

Discriminative Parameter Training of the Extended Particle-Aided Unscented Kalman Filter for Vehicle Localization

Discriminative Parameter Training of the Extended Particle-Aided Unscented Kalman Filter for Vehicle Localization

Adaptive Cooperative Localization Using Relative Position Estimation for Networked Systems With Minimum Number of Communication Links

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

Contact Info

Product

Resources

About