Robust Visual Simultaneous Localization and Mapping for MAV Using Smooth Variable Structure Filter

2015 ◽  
pp. 557-569 ◽  
Author(s):  
Abdelkrim Nemra ◽  
Luis M. Bergasa ◽  
Elena López ◽  
Rafael Barea ◽  
Alejandro Gómez ◽  
...  
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Variable Structure ◽  
Localization And Mapping ◽  
Smooth Variable Structure Filter

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 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.

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 survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

scholarly journals Research on Laser-Visual Fusion-based Simultaneous Localization and Mapping

2020 ◽  
Vol 1682 ◽  
pp. 012049
Author(s):  
Jianjie Zhenga ◽  
Haitao Zhang ◽  
Kai Tang ◽  
Weidi Kong
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Localization And Mapping

scholarly journals Modelling Software Architecture for Visual Simultaneous Localization and Mapping

Automation ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 48-61
Author(s):  
Bhavyansh Mishra ◽  
Robert Griffin ◽  
Hakki Erhan Sevil
Keyword(s):  
System Integration ◽  
Ad Hoc ◽  
Simultaneous Localization And Mapping ◽  
Software Systems ◽  
Model Driven ◽  
Performance Improvements ◽  
Software Model ◽  
Localization And Mapping ◽  
Stereo Cameras ◽  
Core Components

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.

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