• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.288-295
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• 2011

The paper proposes a localization method of an underwater robot using Monte Carlo Localization(MCL) approach. Localization is one of the fundamental basics for autonomous navigation of an underwater robot. The proposed method resolves the problem of accumulation of position error which is fatal to dead reckoning method. It deals with uncertainty of the robot motion and uncertainty of sensor data in probabilistic approach. Especially, it can model the nonlinear motion transition and non Gaussian probabilistic sensor characteristics. In the paper, motion model is described using Euler angles to utilize the MCL algorithm for position estimation of an underwater robot. Motion model and sensor model are implemented and the performance of the proposed method is verified through simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.450-456
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• 2006

Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Although MCL is capable of estimating the robot pose even for a completely unknown initial pose in the known environment, it takes considerable time to give an initial pose estimate because the number of random samples is usually very large especially for a large-scale environment. For practical implementation of MCL, therefore, a reduction in sample size is desirable. This paper presents a novel approach to reducing the number of samples used in the particle filter for efficient implementation of MCL. To this end, the topological information generated through the thinning technique, which is commonly used in image processing, is employed. The global topological map is first created from the given grid map for the environment. The robot then scans the local environment using a laser rangefinder and generates a local topological map. The robot then navigates only on this local topological edge, which is likely to be similar to the one obtained off-line from the given grid map. Random samples are drawn near the topological edge instead of being taken with uniform distribution all over the environment, since the robot traverses along the edge. Experimental results using the proposed method show that the number of samples can be reduced considerably, and the time required for robot pose estimation can also be substantially decreased without adverse effects on the performance of MCL.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1259-1265
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• 2021

In the present study, a large-area hybrid gamma imaging system was designed by adopting coded aperture imaging on the basis of a large-area Compton camera to achieve high imaging performance throughout a broad energy range (100-2000 keV). The system consisting of a tungsten coded aperture mask and monolithic NaI(Tl) scintillation detectors was designed through a series of Geant4 Monte Carlo radiation transport simulations, in consideration of both imaging sensitivity and imaging resolution. Then, the performance of the system was predicted by Geant4 Monte Carlo simulations for point sources under various conditions. Our simulation results show that the system provides very high imaging sensitivity (i.e., low values for minimum detectable activity, MDA), thus allowing for imaging of low-activity sources at distances impossible with coded aperture imaging or Compton imaging alone. In addition, the imaging resolution of the system was found to be high (i.e., around 6°) over the broad energy range of 59.5-1330 keV.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.667-675
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• 2013

This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.901-905
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• 2005

Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Much research has been done to improve performance of MCL so far. Although MCL is capable of estimating the robot pose even for a completely unknown initial pose in the known environment, it takes considerable time to give an initial estimate because the number of random samples is usually very large especially for a large-scale environment. For practical implementation of the MCL, therefore, a reduction in sample size is desirable. This paper presents a novel approach to reducing the number of samples used in the particle filter for efficient implementation of MCL. To this end, the topological information generated off- line using a thinning method, which is commonly used in image processing, is employed. The topological map is first created from the given grid map for the environment. The robot scans the local environment using a laser rangefinder and generates a local topological map. The robot then navigates only on this local topological edge, which is likely to be the same as the one obtained off- line from the given grid map. Random samples are drawn near the off-line topological edge instead of being taken with uniform distribution, since the robot traverses along the edge. In this way, the sample size required for MCL can be drastically reduced, thus leading to reduced initial operation time. Experimental results using the proposed method show that the number of samples can be reduced considerably, and the time required for robot pose estimation can also be substantially decreased.

• Progress in Medical Physics
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• v.5 no.1
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• pp.41-53
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• 1994

The accurate localization of electrical sources in the brain is one of the most important questions in EEG, especially in the analysis of evoked responses and of epileptiform spike activity. A detailed simulation study of single dipole source estimation based on EEG is given in this paper. The effects of dipole model parameters on single dipole source tracing in EEG are examined in some detail using the Monte Carlo simulation. The error of source localization is found to be greatly influenced by how the electrodes are distributed over the head and the number of them.

• Journal of Biomedical Engineering Research
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• v.15 no.1
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• pp.71-76
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• 1994

In the source localization using single dipole model, the influence of the number of electrodes, the position and direction of a single dipole on the relation between S/W ratio and dipole parameter estimation errors is important. Monte Carlo simulation was used to investigate this influence. The forward problem was calculated using three spherical shell model, and dipole parameters were optimized by means of simplex method. As the number of electrodes became large, as the dipole went from midbrain to cortex, and as the direction of dipole changed from radial to tangential, the average and standard deviation of estimation errors became small.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.357-361
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• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.433-438
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• 2007

This paper presents a distance mapping-based localization method with incomplete data which means partially observed data. We make three contributions. First, we propose the use of Multi Dimensional Scaling (MDS) for multi-robot localization. Second, we formulate the problem to accomodate partial observations common in multi-robot settings. We solve the resulting optimization problem using #Scaling by Majorizing a Complicated function (SMACOF)#, a popular algorithm fur iterative MDS. Third, we not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.420-427
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• 2010

This paper describes a localization method based on Monte Carlo Localization approach for a mobile robot. The method uses range data which are measured from ultrasound transmitting beacons whose locations are given a priori. The ultrasound receiver on-board a robot detects the range from the beacons. The method requires several beacons, theoretically over three. The method proposes a sensor model for the range sensing based on statistical analysis of the sensor output. The experiment uses commercialized beacons and detector which are used for trilateration localization. The performance of the proposed method is verified through real implementation. Especially, it is shown that the performance of the localization degrades as the sensor update rate decreases compared with the MCL algorithm update rate. Though the method requires exact location of the beacons, it doesn't require geometrical map information of the environment. Also, it is applicable to estimation of the location of both the beacons and robot simultaneously.