Parallel implementation of mobile robotic self-localization

Authors:
Priscila Tiemi Maeda Saito;Denis Fernando Wolf;Kalinka R. L. J. C. Branco;Ricardo José Sabatine
Affiliations:
University of Sao Paulo, São Carlos, SP - Brazil;University of Sao Paulo, São Carlos, SP - Brazil;University of Sao Paulo, São Carlos, SP - Brazil;University of Sao Paulo, São Carlos, SP - Brazil
Venue:
Proceedings of the 2009 International Conference on Hybrid Information Technology
Year:
2009

Citing 7
Cited 0

Efficient parallel algorithm for robot inverse dynamics computation

IEEE Transactions on Systems, Man and Cybernetics
An approach to parallel processing of dynamic robot models

International Journal of Robotics Research
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing

PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
MPI: The Complete Reference

MPI: The Complete Reference
Parallel, Decentralized Spatial Mapping for Robot Navigation and Path Planning

PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
User transparency: a fully sequential programming model for efficient data parallel image processing: Research Articles

Concurrency and Computation: Practice & Experience
Parallel Programming in C with MPI and OpenMP

Parallel Programming in C with MPI and OpenMP

Quantified Score

Hi-index	0.00

Visualization

Abstract

Self-localization is a fundamental problem in mobile robotics. It consists of estimating the position of a robot given a map of the environment and information obtained by sensors. Among the algorithms used to address this issue, the Monte Carlo technique has obtained a considerable attention by the scientific community due to its simplicity and precision. Monte Carlo localization is a sample-based technique that estimates robot's pose using a probability density function represented by samples (particles). The complexity of this algorithm scales proportionally to the number of particles used. The larger the environment, the more particles are required for robot localization. This fact limits the use of this algorithm to medium size environments. In order to improve the efficiency of the Monte Carlo technique and allow it to be used in large environments we propose a parallel implementation. Our implementation is based on OpenMP and MPI message passing interface. Experimental results are used to show the efficiency of our approach.