Differential evolution solution to the SLAM problem (original) (raw)
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Differential evolution approach to the grid-based localization and mapping problem
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
A new solution to the Simultaneous Localization and Modelling problem is presented. It is based on the stochastic search of solutions in the state space to the global localization problem by means of a differential evolution algorithm. A non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at a local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In a second step the aligned laser measures together with the corrected robot poses are use to detect when the robot is revisiting a previously crossed area. Once a cycle is detected, the Evolutive Localization Filter is used again to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment. The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.
Mobile Robot Global Localization using an Evolutionary MAP Filter
Journal of Global Optimization, 2007
A new algorithm based on evolutionary computation concepts is presented in this paper. This algorithm is a non linear evolutive filter known as the Evolutive Localization Filter (ELF) which is able to solve the global localization problem in a robust and efficient way. The proposed algorithm searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information up to date. The population evolves by using the log-likelihood of each candidate pose according to the observation and the motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The algorithm has been tested on a mobile robot equipped with a laser range finder to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.
Evolutionary Filter for Mobile Robot Global Localization
2007 IEEE International Symposium on Intelligent Signal Processing, 2007
Mobile robot global localization aims to determine the robot's pose in a known environment in absence of initial robot's pose information. This article presents an evolutive localization algorithm known as Evolutive Localization Filter(ELF). Based on evolutionary computation concepts, the proposed algorithm search stochastically along the state space the best robot's pose estimate. The set of pose solutions (the population) represents the most likely areas according the perception and motion information received. The population evolves by using the observation and motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The resulting global localization module has been tested in a mobile robot equipped with a laser range finder. Experiments demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.
E-SLAM solution to the grid-based Localization and Mapping problem
2007 IEEE International Symposium on Intelligent Signal Processing, 2007
A new solution to the Simultaneous Localization and Modelling problem is presented. It is based on the stochastic search of solutions in the state space to the global localization problem by means of a differential evolution algorithm. A non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at a local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In a second step the aligned laser measures together with the corrected robot poses are use to detect when the robot is revisiting a previously crossed area. Once a cycle is detected, the Evolutive Localization Filter is used again to reestimate the robot poses in order to integrate the sensor measures in the global map of the environment. The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.
Evolutionary filter for robust mobile robot global localization
Robotics and Autonomous Systems, 2006
Mobile robot global localization aims to determine the robot's pose in a known environment in absence of initial robot's pose information. This article presents an evolutive localization algorithm known as Evolutive Localization Filter (ELF). Based on evolutionary computation concepts, the proposed algorithm search stochastically along the state space the best robot's pose estimate. The set of pose solutions (the population) represents the most likely areas according the perception and motion information received. The population evolves by using the observation and motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The resulting global localization module has been integrated successfully in a mobile robot equipped with a laser range finder. Experiments demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.
2015
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.
Sensors, 2015
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.
L1-norm global localization based on a differential evolution filter
2009
Global localization methods deal with the estimation of a mobile robot's pose assuming no prior state information about it and a complete a priori knowledge of the environment where the mobile robot is going to be localized. Most existent algorithms are based on the minimization of a L2-norm loss function. However, the use of a L1-norm offers some alternative advantages. The present work explores the use of a L1-norm together with an Evolutive Localization Filter to determine its efficiency 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 the L1-norm approach.
Differential Evolution Markov Chain Filter for Global Localization
Journal of Intelligent & Robotic Systems, 2015
A key challenge for an autonomous mobile robot is to estimate its location according to the available information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evolution 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 Differential Evolution method to solve efficiently many optimization problems. In this work, we have combined 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.