scholarly journals Coevolution Based Adaptive Monte Carlo Localization (CEAMCL)

10.5772/5634 ◽  
2004 ◽  
Vol 1 (3) ◽  
pp. 19 ◽  
Author(s):  
Luo Ronghua ◽  
Hong Bingrong
Keyword(s):  
Monte Carlo ◽  
Adaptive Monte Carlo ◽  
Monte Carlo Localization

scholarly journals Self-adaptive Monte Carlo localization for mobile robots using range finders

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Lei Zhang ◽  
René Zapata ◽  
Pascal Lépinay
Keyword(s):  
Monte Carlo ◽  
Mobile Robots ◽  
Position Tracking ◽  
Localization Algorithm ◽  
Global Localization ◽  
Adaptive Monte Carlo ◽  
Local Sample ◽  
Monte Carlo Localization ◽  
Sample Set ◽  
Self Adaptive

SUMMARYIn order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.

Data-Driven, Ground Truth-Free Tuning of an Adaptive Monte Carlo Localization Method for Urban Scenarios

2021 ◽  
Author(s):  
Jessica Giovagnola ◽  
Davide Rigamonti ◽  
Matteo Corno ◽  
Weidong Chen ◽  
Sergio M. Savaresi
Keyword(s):  
Monte Carlo ◽  
Ground Truth ◽  
Data Driven ◽  
Adaptive Monte Carlo ◽  
Localization Method ◽  
Monte Carlo Localization

Subsectional Adaptive Monte Carlo Localization for Humanoid Soccer Robot

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 652 ◽  
Author(s):  
Wei HONG ◽  
Changjiu ZHOU ◽  
Yantao TIAN
Keyword(s):  
Monte Carlo ◽  
Soccer Robot ◽  
Adaptive Monte Carlo ◽  
Monte Carlo Localization

scholarly journals ROS-based localization of a race vehicle at high-speed using LIDAR

2019 ◽  
Vol 95 ◽  
pp. 04002 ◽  
Author(s):  
Tim Stahl ◽  
Alexander Wischnewski ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo ◽  
Performance Evaluation ◽  
Kalman Filter ◽  
Pose Estimation ◽  
High Speed ◽  
Position Estimation ◽  
Redundant Information ◽  
Adaptive Monte Carlo ◽  
High Speeds ◽  
Monte Carlo Localization

An approach for LIDAR-based localization at high speeds is presented. In the proposed framework, the laser pose estimation is treated as a parallel redundant information, which is fused in an adjacent Kalman filter. The measurement and motion update step of the ROS-based adaptive Monte Carlo localization package is modified, in order to meet the requirements of a high-speed race scenario. Thereby, the key focus is on computational efficiency and the adaptation to characteristics arising at high speeds and at the limits of handling. An introspective performance evaluation monitors the position estimation process and labels generated outputs for adjacent components accordingly. The effectiveness of the proposed algorithm is illustrated in a real world high-speed experiment, autonomously driving a race vehicle – the DevBot – in a typical race environment.

scholarly journals An Extended Analysis on Tuning The Parameters of Adaptive Monte Carlo Localization ROS Package in an Automated Guided Vehicle

2021 ◽  
Author(s):  
Wallace Pereira Neves dos Reis ◽  
Guilherme José da Silva ◽  
Orides Morandin Junior ◽  
Kelen Cristiane Teixeira Vivaldini
Keyword(s):  
Monte Carlo ◽  
Indoor Localization ◽  
Parameter Tuning ◽  
Automated Guided Vehicle ◽  
Adaptive Monte Carlo ◽  
Pose Tracking ◽  
Extended Analysis ◽  
Monte Carlo Localization ◽  
Tuning Process ◽  
Best Parameter

Abstract With a growth tendency, the employment of the Adaptive Monte Carlo Localization (AMCL) Robot Operational System (ROS) package does not reflect a more indepth discussion on its parameters tuning process. The authors usually do not describe it. This work aims to extend the analysis of the package’s parameters distinct influence in an Automated Guided Vehicle (AGV) indoor localization. The experiments test parameters of the filter, the laser model, and the odometry model. Extending the previous analysis of seven parameters, the present research discusses another ten from the 22 configurable parameters of the package. An external visual vehicle pose tracking is used to compare the pose estimation from the localization package. Although the article does not propose the best parameter tuning, its results discuss how each tested parameter affects the localization. The paper’s contribution is discussing the parameters variation impact on the AGV localization. It may help new researchers in the AMCL ROS package parameter tuning process. The results show minor changes in the default parameters can improve the localization results, even modifying one parameter at a time.

scholarly journals MAPPING AND POSITIONING SYSTEM ON OMNIDIRECTIONAL ROBOT USING SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) METHOD BASED ON LIDAR

2021 ◽  
Vol 83 (6) ◽  
pp. 41-52
Author(s):  
Achmad Akmal Fikri ◽  
Lilik Anifah
Keyword(s):  
Monte Carlo ◽  
System Integration ◽  
Simultaneous Localization And Mapping ◽  
Autonomous Robot ◽  
Positioning System ◽  
Adaptive Monte Carlo ◽  
Surrounding Environment ◽  
Localization And Mapping ◽  
Mapping System ◽  
Monte Carlo Localization

The main problem from autonomous robot for navigation is how the robot able to recognize the surrounding environment and know this position. These problems make this research weakness and become a challenge for further research. Therefore, this research focuses on designing a mapping and positioning system using Simultaneous Localization and Mapping (SLAM) method which is implemented on an omnidirectional robot using a LiDAR sensor. The proposes of this research  are mapping system using the google cartographer algorithm combined with the eulerdometry method, eulerdometry is a combination of odometry and euler orientation from IMU sensor, while the positioning system uses the Adaptive Monte Carlo Localization (AMCL) method combined with the eulerdometry method. Testing is carried out by testing the system five times from each system, besides that testing is also carried out at each stage, testing on each sensor used such as the IMU and LiDAR sensors, and testing on system integration, including the eulerdometry method, mapping system and positioning system. The results on the mapping system showed optimal results, even though there was still noise in the results of the maps created, while the positioning system test got an average RMSE value from each map created of 278.55 mm on the x-axis, 207.37 mm on the y-axis, and 4.28o on the orientation robot.

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