Simultaneous localization and mapping algorithm for unmanned ground vehicle with SVSF filter

2016 ◽  
Author(s):  
Fethi Demim ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Zakaria Mehal ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Mapping Algorithm ◽  
Localization And Mapping

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 A novel approach for the global localization problem

2012 ◽  
Vol 22 ◽  
pp. 106-112
Author(s):  
Alfredo Toriz ◽  
Abraham Sánchez ◽  
Maria A. Osorio
Keyword(s):  
Data Structure ◽  
Simultaneous Localization And Mapping ◽  
Random Tree ◽  
Localization Problem ◽  
Global Localization ◽  
Mapping Algorithm ◽  
B Splines ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Safe Region

This paper describes a simultaneous planning localization and mapping (SPLAM) methodology focussed on the global localization problem, where the robot explores the environment efficiently and also considers the requisites of the simultaneous localization and mapping algorithm. The method is based on the randomized incremental generation of a data structure called Sensor-based Random Tree, which represents a roadmap of the explored area with an associated safe region. A continuous localization procedure based on B-Splines features of the safe region is integrated in the scheme.

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