scholarly journals An ASVSF-SLAM Algorithm with Time-Varying Noise Statistics Based on MAP Creation and Weighted Exponent

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yingzhong Tian ◽  
Heru Suwoyo ◽  
Wenbin Wang ◽  
Long Li
Keyword(s):  
Continuous System ◽  
Variable Structure ◽  
Measurement Noise ◽  
Time Varying ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Filter Divergence ◽  
Correction Step ◽  
Noise Statistics ◽  
Smooth Variable Structure Filter

The probability-based filtering method has been extensively used for solving the simultaneous localization and mapping (SLAM) problem. Generally, the standard filter utilizes the system model and prior stochastic information to approximate the posterior state. However, in the real-time situation, the noise statistics properties are relatively unknown, and the system is inaccurately modeled. Thus the filter divergence might occur in the integration system. Moreover, the expected accuracy might be challenging to be reached due to the absence of the responsive time-varying of both the process and measurement noise statistic which naturally can enlarge the uncertainty in the continuous system. Consequently, the traditional strategy needs to be improved aiming to provide an ability to estimate those properties. In order to accomplish this issue, the new adaptive filter is proposed in this paper, termed as an adaptive smooth variable structure filter (ASVSF). Sequentially, the improved SVSF is derived and implemented; the process and measurement noise statistics are estimated by utilizing the maximum a posteriori (MAP) creation and the weighted exponent concept, and the covariance correction step is added based on the divergence suppression concept. In this paper the ASVSF is applied to overcome the SLAM problem of an autonomous mobile robot; henceforth it is abbreviated as an ASVSF-SLAM algorithm. It is simulated and compared to the classical algorithm. The simulation results demonstrated that the proposed algorithm has better performance, stability, and effectiveness.

scholarly journals A MAPAEKF-SLAM ALGORITHM WITH RECURSIVE MEAN AND COVARIANCE OF PROCESS AND MEASUREMENT NOISE STATISTIC

SINERGI ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 37
Author(s):  
Heru Suwoyo ◽  
Yingzhong Tian ◽  
Wenbin Wang ◽  
Md Musabbir Hossain ◽  
Long Li
Keyword(s):  
Time Integration ◽  
Measurement Noise ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Filter Performance ◽  
Whole Process ◽  
Noise Statistic ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Noise Statistics

The most popular filtering method used for solving a Simultaneous Localization and Mapping is the Extended Kalman Filter. Essentially, it requires prior stochastic knowledge both the process and measurement noise statistic. In order to avoid this requirement, these noise statistics have been defined at the beginning and kept to be fixed for the whole process. Indeed, it will satisfy the desired robustness in the case of simulation. Oppositely, due to the continuous uncertainty affected by the dynamic system under time integration, this manner is strongly not recommended. The reason is, improperly defined noise will not only degrade the filter performance but also might lead the filter to divergence condition. For this reason, there has been a strong manner well-termed as an adaptive-based strategy that commonly used to equip the classical filter for having an ability to approximate the noise statistic. Of course, by knowing the closely responsive noise statistic, the robustness and accuracy of an EKF can increase. However, most of the existed Adaptive-EKF only considered that the process and measurement noise statistic are characteristically zero-mean and responsive covariances. Accordingly, the robustness of EKF can still be enhanced. This paper presents a proposed method named as a MAPAEKF-SLAM algorithm used for solving the SLAM problem of a mobile robot, Turtlebot2. Sequentially, a classical EKF was estimated using Maximum a Posteriori. However, due to the existence of unobserved value, EKF was also smoothed one time based on the fixed-interval smoothing method. This smoothing step aims to keep-up the derivation process under MAP creation. Realistically, this proposed method was simulated and compared to the conventional one. Finally, it has been showing better accuracy in terms of Root Mean Square Error (RMSE) of both Estimated Map Coordinate (EMC) and Estimated Path Coordinate (EPC).       

A new adaptive smooth variable structure filter SLAM algorithm for unmanned vehicle

2017 ◽  
Author(s):  
Fethi Demim ◽  
Abdelghani Boucheloukh ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Variable Structure ◽  
Unmanned Vehicle ◽  
Slam Algorithm ◽  
Smooth Variable Structure Filter

scholarly journals Cooperative Visual SLAM based on Adaptive Covariance Intersection

2018 ◽  
Vol 2 (3) ◽  
pp. 151 ◽  
Author(s):  
Fethi Denim ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Abdelghani Boucheloukh ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Stereo Vision ◽  
Variable Structure ◽  
Uncertain System ◽  
Visual Slam ◽  
Creative Commons ◽  
Localization And Mapping ◽  
Covariance Intersection ◽  
Position Data ◽  
Modelling Uncertainties ◽  
Slam Algorithm

Simultaneous localization and mapping (SLAM) is an essential capability for Unmanned Ground Vehicles (UGVs) travelling in unknown environments where globally accurate position data as GPS is not available. It is an important topic in the autonomous mobile robot research. This paper presents an Adaptive De-centralized Cooperative Vision-based SLAM solution for multiple UGVs, using the Adaptive Covariance Intersection (ACI) supported by a stereo vision sensor. In recent years, SLAM problem has gotten a specific consideration, the most commonly used approaches are the EKF-SLAM algorithm and the FAST-SLAM algorithm. The primary, which requires an accurate process and an observation model, suffers from the linearization problem. The last mentioned is not suitable for real-time implementation. In our work, the Visual SLAM (VSLAM) problem could be solved based on the Smooth Variable Structure Filter (SVSF) is proposed. This new filter is robust and stable to modelling uncertainties making it suitable for UGV localization and mapping problem. This new strategy retains the near optimal performance of the SVSF when applied to an uncertain system, it has the added benefit of presenting a considerable improvement in the robustness of the estimation process. All UGVs will add data features sorted by the ACI that estimate position on the global map. This solution gives, as a result, a large reliable map constructed by a group of UGVs plotted on it. This paper presents a Cooperative SVSF-VSLAM algorithm that contributes to solve the Adaptive Cooperative Vision SLAM problem for multiple UGVs. The algorithm was implemented on three mobile robots Pioneer 3-AT, using stereo vision sensors. Simulation results show eciency and give an advantage to our proposed algorithm, compared to the Cooperative EKF-VSLAM algorithm mainly concerning the noise quality.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Simultaneous localization and mapping navigation of unmanned ground vehicle based on second-order smooth variable structure filter with improved technique to combat fading for advanced wireless communications

2020 ◽  
pp. 095965182096572
Author(s):  
Abdelkader Mosbah ◽  
Fethi Demim ◽  
Ali Mansoul ◽  
Mustapha Benssalah ◽  
Abdelkrim Nemra
Keyword(s):  
Wireless Communications ◽  
Wireless Communication ◽  
Mobile Robot ◽  
Communication Systems ◽  
Simultaneous Localization And Mapping ◽  
Variable Structure ◽  
Second Order ◽  
Mapping Problem ◽  
Localization And Mapping ◽  
Smooth Variable Structure Filter

Simultaneous localization and mapping is very essential for autonomous navigation when the mobile robot is navigating in unknown environment without a global positioning system. Various techniques to solve the simultaneous localization and mapping problem have been extensively studied using the combination of low-cost sensors. Most of the work in mobile robotics still consists of finding solutions to problems in data exchange between mobile robot and communication control station, which is a challenging task. In fact, communication systems impose severe constraints in terms of channel capacity and transmission quality, because the transmission channel in communication systems is undergoing at the different physical phenomena like scattering, diffusion and diffraction, which occur interference and multiple path effects in wireless communications, while keeping these effects levels low. This article describes a simultaneous localization and mapping problem based on second-order smooth variable structure filter embedded in mobile robot equipped with a sensor for data wireless collection. The inclusion of the control in environments outside the mobile robot field of view can make the wireless communication simultaneous localization and mapping process much more difficult to find a solution under realistic conditions. In order to solve the simultaneous localization and mapping issue and to mitigate the fading phenomena, which affect the quality of service in advanced wireless communication systems, we use a new approach to combat the fading effect without requiring any statistical knowledge of the propagation channel parameters. Several experiments have been done in real-world applications, and good performances were obtained using a second-order smooth variable structure filter–simultaneous localization and mapping algorithm–based wireless communication.

scholarly journals A SLAM Algorithm Based on Adaptive Cubature Kalman Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Fei Yu ◽  
Qian Sun ◽  
Chongyang Lv ◽  
Yueyang Ben ◽  
Yanwei Fu
Keyword(s):  
Kalman Filter ◽  
A Priori ◽  
Statistical Parameters ◽  
Estimation Errors ◽  
Adaptive Estimator ◽  
Practical Applications ◽  
Localization And Mapping ◽  
Cubature Kalman Filter ◽  
Slam Algorithm ◽  
Noise Statistics

We need to predict mathematical model of the system and a priori knowledge of the noise statistics when traditional simultaneous localization and mapping (SLAM) solutions are used. However, in many practical applications, prior statistics of the noise are unknown or time-varying, which will lead to large estimation errors or even cause divergence. In order to solve the above problem, an innovative cubature Kalman filter-based SLAM (CKF-SLAM) algorithm based on an adaptive cubature Kalman filter (ACKF) was established in this paper. The novel algorithm estimates the statistical parameters of the unknown system noise by introducing the Sage-Husa noise statistic estimator. Combining the advantages of the CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible.

scholarly journals ADAPTIVE DEVELOPMENT OF SVSF FOR A FEATURE-BASED SLAM ALGORITHM USING MAXIMUM LIKELIHOOD ESTIMATION AND EXPECTATION MAXIMIZATION

2021 ◽  
Vol 22 (1) ◽  
pp. 269-286
Author(s):  
Heru Suwoyo ◽  
Yingzhong Tian ◽  
Wenbing Wang ◽  
Long Li ◽  
Andi Adriansyah ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Expectation Maximization ◽  
Symmetric Matrix ◽  
Likelihood Estimation ◽  
Variable Structure ◽  
Robust Estimator ◽  
Noise Statistic ◽  
Localization And Mapping ◽  
Slam Algorithm

ABSTRACT:  The smooth variable structure filter (SVSF) has been considered as the robust estimator. Like other filters, the SVSF needs an accurate system model and known noise statistics to approximate the posterior state. Unfortunately, the system cannot be accurately modeled, and the noise statistic is unknown in the real application. For these reasons, the performance of SVSF might be decreased or even led to divergence. Therefore, the enhancement of SVSF is required. This paper presents an Adaptive SVSF. Initially, SVSF is smoothed. To provide the ability to estimate the noise statistic, ASVSF is then derived based on maximum likelihood estimation (MLE) and expectation-maximization (EM). Additionally, the unbiased noise statistic is also approached. However, its covariance is complicatedly formulated. It might cause a negative definite symmetric matrix. Therefore, it is tuned based on the innovation covariance estimator (ICE). The ASVSF is designed to solve the online problem of Simultaneous Localization and Mapping (SLAM). Henceforth, it is termed as the ASVSF-SLAM algorithm. The proposed algorithm showed better accuracy and stability compared to the conventional algorithm in terms of root mean square error (RMSE) for both Estimated Path Coordinate (EPC) and Estimated Map Coordinate (EMC). ABSTRAK: Penapis struktur bolehubah lembut (SVSF) telah dianggap sebagai penganggar teguh. Seperti penapis lain, SVSF memerlukan model sistem yang tepat dan statistik hingar yang diketahui bagi menganggar keadaan posterior. Malangnya, sistem tidak dapat dimodelkan dengan tepat dan statistik hingar tidak diketahui dalam aplikasi sebenar. Atas sebab-sebab ini, prestasi SVSF mungkin berkurangan, bahkan berbeza. Oleh itu, memperbaharui SVSF adalah perlu. Kajian ini adalah mengenai SVSF Mudah Suai. Pada awalnya, SVSF dilembutkan. Bagi menyediakan keupayaan anggaran statistik hinggar, ASVSF dihasilkan terlebih dahulu berdasarkan anggaran kemungkinan maksimum (MLE) dan maksimum-harapan (EM). Tambahan, statistik hinggar yang tidak berat sebelah juga dibuat. Walau bagaimanapun, rumusan formula kovarians ini adalah kompleks. Ini mungkin menyebabkan matriks simetri menjadi negatif. Oleh itu, ia diselaraskan berdasarkan penganggar kovarians inovasi (ICE). ASVSF dibina bagi menyelesaikan masalah dalam talian Penempatan dan Pemetaan Serentak (SLAM) dalam talian. Oleh itu, ia disebut sebagai algoritma ASVSF-SLAM. Algoritma yang dicadangkan ini menunjukkan ketepatan dan kestabilan yang lebih baik berbanding algoritma konvensional dari segi ralat punca min kuasa dua (RMSE) bagi kedua-dua Koordinat Anggaran Laluan (EPC) dan Anggaran Koordinat Peta (EMC).

scholarly journals Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401773665 ◽  
Author(s):  
Demim Fethi ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Mustapha Hamerlain
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Simultaneous Localization And Mapping ◽  
Variable Structure ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Localization And Mapping ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Smooth Variable Structure Filter

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle’s kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer [Formula: see text]. The obtained results using smooth variable structure filter–simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter–simultaneous localization and mapping positions.

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