scholarly journals An extended analysis on tuning the parameters of Adaptive Monte Carlo Localization ROS package in an automated guided vehicle

Author(s):  
Wallace Pereira Neves dos Reis ◽  
Guilherme José da Silva ◽  
Orides Morandin Junior ◽  
Kelen Cristiane Teixeira Vivaldini
2021 ◽  
Author(s):  
Wallace Pereira Neves dos Reis ◽  
Guilherme José da Silva ◽  
Orides Morandin Junior ◽  
Kelen Cristiane Teixeira Vivaldini

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.


Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Lei Zhang ◽  
René Zapata ◽  
Pascal Lépinay

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.


2021 ◽  
Author(s):  
Jessica Giovagnola ◽  
Davide Rigamonti ◽  
Matteo Corno ◽  
Weidong Chen ◽  
Sergio M. Savaresi

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 652 ◽  
Author(s):  
Wei HONG ◽  
Changjiu ZHOU ◽  
Yantao TIAN

2019 ◽  
Vol 95 ◽  
pp. 04002 ◽  
Author(s):  
Tim Stahl ◽  
Alexander Wischnewski ◽  
Johannes Betz ◽  
Markus Lienkamp

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.


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