L1-L2-norm comparison in global localization of mobile robots

Authors:
L. Moreno;D. Blanco;M. L. Muñoz;S. Garrido
Affiliations:
Robotics Lab, Carlos III University of Madrid, Madrid, Spain;Robotics Lab, Carlos III University of Madrid, Madrid, Spain;Faculty of Computer Science, Polytechnic University of Madrid, Madrid, Spain;Robotics Lab, Carlos III University of Madrid, Madrid, Spain
Venue:
Robotics and Autonomous Systems
Year:
2011

Citing 5
Cited 2

Modeling a dynamic environment using a Bayesian multiple hypothesis approach

Artificial Intelligence
Robust Monte Carlo localization for mobile robots

Artificial Intelligence
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)

Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Estimating the absolute position of a mobile robot using position probability grids

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2

Cascaded Evolutionary Estimator for Robot Localization

International Journal of Applied Evolutionary Computation
Kullback-Leibler divergence-based global localization for mobile robots

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The global localization methods deal with the estimation of the pose of a mobile robot assuming no prior state information about the pose and a complete a priori knowledge of the environment where the mobile robot is going to be localized. Most existing algorithms are based on the minimization of an L2-norm loss function. In spite of the extended use of the L2-norm, the use of the L1-norm offers some alternative advantages. The present work compares the L1-norm and the L2-norm with the same basic optimization mechanism to determine the advantages of each norm when applied to the global localization problem. The algorithm has been tested subject to different noise levels to demonstrate the accuracy, effectiveness, robustness, and computational efficiency of both L1-norm and L2-norm approaches.