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

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.

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
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