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

Moreno, Luís; Blanco, Dolores; Muñoz, Margarita; Garrido, Santiago

doi:10.1016/j.robot.2011.04.006

Cited by 15 publications

(10 citation statements)

References 22 publications

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“…These errors are slightly lower than those obtained in our previous work [5,24], and they are low enough to conclude that the GL problem is efficiently solved.…”

Section: Architectural Plancontrasting

confidence: 60%

“…The Kullback-Leibler divergence is an appropriate metric to deal with different types of occlusions [5]. The Manhattan distance (L1-norm) is a more suitable approach in environments with dynamic obstacles [24]. Donoso et al [25] have used the Hausdorff distance.…”

Section: Related Workmentioning

confidence: 99%

“…It is usually defined in the interval [0.4, 0.9], with an empirical upper limit equal to 1 [2]. Zaharie [55] has restricted the lower limit of F. This parameter will be fixed to 0.7 in the experiments according to the optimum values found in our previous work [24].…”

Section: Differential Evolution Algorithm For Glmentioning

confidence: 99%

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Differential Evolution Markov Chain Filter for Global Localization

Moreno

Alonso-Martín

Muñoz

et al. 2015

J Intell Robot Syst

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A key challenge for an autonomous mobile robot is to estimate its location according to the avail able information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evo lution method that solves this problem in 2D and 3D environments. The robot's pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Dif ferential Evolution method to solve efficiently many optimization problems. In this work, we have com bined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.

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“…These errors are slightly lower than those obtained in our previous work [5,24], and they are low enough to conclude that the GL problem is efficiently solved.…”

Section: Architectural Plancontrasting

confidence: 60%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Differential Evolution Markov Chain Filter for Global Localization

Moreno

Alonso-Martín

Muñoz

et al. 2015

J Intell Robot Syst

View full text Add to dashboard Cite

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“…The term · 1,2 provides one of the possibilities of how to express the self-similarity ratio of the surveyed course of two functions. Over the several last decades, advantages and disadvantages of the methods that are based on the L1-or L2-norm were profusely described in technical literature of many scientific branches, for example, in medicine (Ding 2009), engineering (Parsopoulos et al 2004), forecasting (Dielman 1986), statistics (Květoň 1987), computer sciences and image processing (Zhu et al 2006;Fuchs 1984;Fu et al 2006;Yan et al 2009), robotics (Moreno et al 2011). The L2-norm, also called least square method (LSM), represents a widely used computational scheme for estimation of parameters.…”

Section: L1-norm L2-norm Related Workmentioning

confidence: 99%

A Comparative Study: L1-Norm Vs. L2-Norm; Point-to-Point Vs. Point-to-Line Metric; Evolutionary Computation Vs. Gradient Search

Moravec

2015

Applied Artificial Intelligence

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The study presented in this article compares the two most frequently used regularizations in the robotics area: L1-norm and L2-norm, for navigation purposes of an autonomous mobile platform in an inner environment with use of the 2D laser scanner. Sensorial data in a real environment are very often burdened by a noise, which unfavorably affects the classification process. Presented results show behavior of all tested algorithms under conditions in which the sensorial data are loaded by common types of the noise: moving objects, quantizing noise, artificially added noise with different types of characteristics that correspond to potentially real conditions. Basic navigation mechanisms presented here use methods of robust statistics and modern evolutionary optimizers. The methods selected in this study represent the two different types of metrics, commonly called point-to-point and point-to-line. The navigation algorithm that uses L1-norm regularization integrates several different evolutionary algorithms that occupy a position of very efficient optimizers, which, at the same time, do not cut down limits of usability of the tested methods. Correct working parameters settings of all used pose estimators play a key role in the robot pose and heading estimation, therefore, this article is extended by a description of several important working parameters and the way to use them.

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“…Other options have been suggested by different researchers. The Manhattan distance is applied in our previous work to improve the method performance when there are dynamic obstacles [ 29 ]. The Hausdorff distance has been considered by Donoso et al [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots

Alonso-Martín

Moreno

Garrido

et al. 2015

Sensors

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One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot’s pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area.

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

Cited by 15 publications

References 22 publications

Differential Evolution Markov Chain Filter for Global Localization

Differential Evolution Markov Chain Filter for Global Localization

A Comparative Study: L1-Norm Vs. L2-Norm; Point-to-Point Vs. Point-to-Line Metric; Evolutionary Computation Vs. Gradient Search

Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots

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