scholarly journals Construction of Fuzzy Map for Autonomous Mobile Robots Based on Fuzzy Confidence Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jung-Fu Hou ◽  
Yau-Zen Chang ◽  
Ming-Hsi Hsu ◽  
Shih-Tseng Lee ◽  
Chieh-Tsai Wu
Keyword(s):  
Mobile Robots ◽  
Autonomous Mobile Robots ◽  
Alignment Error ◽  
Map Building ◽  
Optimization Task ◽  
Mobile Vehicle ◽  
Localization And Mapping ◽  
Finite Resolution ◽  
Potential Part ◽  
Prediction Strategy

This paper presents the use of fuzzy models to explicitly consider sensor uncertainty and finite resolution in solving the SLAM (simultaneous localization and mapping) problem for autonomous mobile robots. The approach establishes fuzzy confidence models in describing occupied obstacles and available space. The problem is transformed into an optimization task of minimizing the alignment error between newly scanned local fuzzy maps and selected parts of a developing global fuzzy map. In aligning local fuzzy maps into a global fuzzy map, we developed a prediction strategy to crop the most potential part from the sensed local fuzzy maps to be overlapped with the global fuzzy map. A mobile vehicle equipped with a laser range finder, the Hokuyo URG-04LX, is used to demonstrate the procedure of fuzzy map building. Experimental results show that the proposed architecture is effective in generating a comprehensive global fuzzy map, which is suitable for both human comprehension and path design during real-time navigation.

Probabilistic and Count Methods in Map Building for Autonomous Mobile Robots

2000 ◽  
pp. 120-137 ◽  
Author(s):  
Miguel Rodríguez ◽  
José Correa ◽  
Roberto Iglesias ◽  
Carlos V. Regueiro ◽  
Senén Barro
Keyword(s):  
Mobile Robots ◽  
Autonomous Mobile Robots ◽  
Map Building

scholarly journals ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4252 ◽  
Author(s):  
Zhichen Pan ◽  
Haoyao Chen ◽  
Silin Li ◽  
Yunhui Liu
Keyword(s):  
Visual Information ◽  
Environmental Changes ◽  
Point Clouds ◽  
Urban Environments ◽  
Unmanned Vehicles ◽  
High Density ◽  
Map Building ◽  
Mobile Vehicle ◽  
Localization And Mapping ◽  
Laser Range Finders

Map building and map-based relocalization techniques are important for unmanned vehicles operating in urban environments. The existing approaches require expensive high-density laser range finders and suffer from relocalization problems in long-term applications. This study proposes a novel map format called the ClusterMap, on the basis of which an approach to achieving relocalization is developed. The ClusterMap is generated by segmenting the perceived point clouds into different point clusters and filtering out clusters belonging to dynamic objects. A location descriptor associated with each cluster is designed for differentiation. The relocalization in the global map is achieved by matching cluster descriptors between local and global maps. The solution does not require high-density point clouds and high-precision segmentation algorithms. In addition, it prevents the effects of environmental changes on illumination intensity, object appearance, and observation direction. A consistent ClusterMap without any scale problem is built by utilizing a 3D visual–LIDAR simultaneous localization and mapping solution by fusing LIDAR and visual information. Experiments on the KITTI dataset and our mobile vehicle illustrates the effectiveness of the proposed approach.

Robot navigation in dynamic environments using global-appearance descriptors. State of the art

2015 ◽  
Vol 2 (1) ◽  
pp. 2
Author(s):  
Yerai Berenguer Fernández
Keyword(s):  
Mobile Robots ◽  
Visual Information ◽  
State Of The Art ◽  
Robot Navigation ◽  
Continuous Optimization ◽  
Dynamic Environments ◽  
Autonomous Mobile Robots ◽  
Map Building ◽  
Future Directions ◽  
Robot Position

Map building and localization are two impor- tant abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents a state of the art report on map building and localization using global appearance descriptors. In this approach, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance descriptor for each image. Using these descriptors, the robot is able to estimate its location in a map previously built, which is also composed of a set of global appearance descriptors. Several previous investigations that have led to the approach of this research are summarized in this paper, such as researches that compare several methods of creating global appearance descriptors. In these works we observe how the continuous optimization of the algorithms has lead to better estimations of the robot position within the environment. Finally a number of future directions in which researches are currently working are listed. 

scholarly journals Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

2021 ◽  
Vol 2129 (1) ◽  
pp. 012018
Author(s):  
R J Musridho ◽  
H Hasan ◽  
H Haron ◽  
D Gusman ◽  
M A Mohammad
Keyword(s):  
Mobile Robots ◽  
Convergence Analysis ◽  
Firefly Algorithm ◽  
Estimation Error ◽  
Position Estimation ◽  
Autonomous Mobile Robots ◽  
Localization And Mapping ◽  
Robot Position ◽  
Modified Firefly Algorithm ◽  
Over Time

Abstract In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

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