Self-adaptive monte carlo for single-robot and multi-robot localization

In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization using self-adaptive samples, abbreviated as SAMCL. This algorithm is able to solve the multi-robot localization problem as well as the...

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Bibliographic Details
Published in2009 IEEE International Conference on Automation and Logistics pp. 1927 - 1933
Main Authors Lei Zhang, Zapata, R., Lepinay, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Computational complexity
Delay
Localization
Logistics
Mobile robots
Monte Carlo methods
multi-robot
Orbital robotics
position mapping
Robot kinematics
Robot sensing systems
Robotics and automation
Runtime
self-adaptive Monte Carlo localization
similar energy region
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ISBN9781424447947
1424447941
ISSN2161-8151
DOI10.1109/ICAL.2009.5262621

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Summary:In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization using self-adaptive samples, abbreviated as SAMCL. This algorithm is able to solve the multi-robot localization problem as well as the single-robot localization problem. By employing a pre-caching technique to reduce the on-line computational burden, SAMCL is more efficient than regular MCL. We define the concept of Similar Energy Region (SER), which is a set of 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. Thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to need, SAMCL can solve position tracking, global localization and the kidnapped robot problem together. SAMCL can be extended to handle multi-robot localization through a Position Mapping (PM) algorithm. This algorithm enables one robot to calculate its possible positions according to positions of other robots and mutual relations between each other. The validity and the efficiency of our algorithm are demonstrated by experiments carried out with different intentions. Extensive experiment results are also given in this paper.
ISBN:9781424447947
1424447941
ISSN:2161-8151
DOI:10.1109/ICAL.2009.5262621