• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.973-978
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• 2005

In this paper, we investigate an improved mobile robot localization method using Kalman filter. The highlight of the paper lies in the formulation of combined Kalman filter and its application to mobile robot experiment. The combined Kalman filter is a kind of extended Kalman filter which has an extra degree of freedom in Kalman filtering recursion. It consists of the standard Kalman filter, i.e., the predictor-corrector and the perturbation estimator which reconstructs unknown dynamics in the state transition equation of mobile robot. The combined Kalman filter (CKF) enables to achieve robust localization performance of mobile robot in spite of heavy perturbation such as wheel slip and doorsill crossover which results in large odometric errors. Intrinsically, it has the property of integrating the innovation in Kalman filtering, i.e., the difference between measurement and predicted measurement and thus it is so much advantageous in compensating uncertainties which has not been reflected in the state transition model of mobile robot. After formulation of the CKF recursion equation, we show how the design parameters can be determined and how much beneficial it is through simulation and experiment for a two-wheeled mobile robot under indoor GPS measurement system composed of four ultrasonic satellites. In addition, we discuss what should be considered and what prerequisites are needed to successfully apply the proposed CKF in mobile robot localization.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1448-1451
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• 2004

Position estimation is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. To compensate the drawbacks, a new localization method that estimates the absolute position of the mobile robot by using a fixed camera on the ceiling in the corridor is proposed. And also, it can improve the success rate for position estimation using the proposed method, which calculates the real size of an object. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data, but a kind of absolute localization. The effectiveness of the proposed localization scheme is demonstrated through the experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.949-955
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• 2007

This paper proposes an efficient localization algorithm in the RFID sensor space for the precise localization of a mobile robot. The RFID sensor space consists of embedded sensors and a mobile robot. The embedded sensors, that is tags are holding the absolute position data and provide them to the robot which carries a reader and requests the absolute position fur localization. The reader, it is called as antenna usually, gets several tag data at the same time within its readable range. It takes time to read all the tags and to process the data to estimate the position, which is a major factor to deteriorate the localization accuracy. In this paper, an efficient algorithm to estimate the position and orientation of the mobile robot as quickly as possible has been proposed. Along with the algorithm, a new allocation of the tags in the RFID sensor space is also proposed to improve the localization accuracy. The proposed algorithms are demonstrated and verified through the real experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1931-1935
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• 2004

A location estimate problem is critical issues for mobile robot. Because it is basic problem in practical use of the mobile robot which do what, or move where, or reach an aim. Already there are many technologies of robot localization (like GPS, vision, sonar sensor, etc) used on development. But the elevation of accurateness was brought the problem that must consider an increase of a hardware cost and addition electric power in each ways. There is the core in question to develop available and accurate sensing algorithm though it is economical. We used a ultrasonic sensor and was going to implement comparatively accurate localization though economical. Using a sensing data, we could make a grid map and estimate a position of a mobile robot. In this paper, to get a satisfactory answer about this problem using a ultrasonic sensor.

• Journal of Mechanical Science and Technology
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• v.16 no.3
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• pp.302-309
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• 2002

This paper presents a technique for localization of a mobile robot using sonar sensors. Localization is the continual provision of knowledges of position that are deduced from its a priori position estimation. The environment of a robot is modeled by a two-dimensional grid map. We define a physically based sonar sensor model and employ an extended Kalman filter to estimate positions of the robot. Since the approach does not rely on an exact geometric model of an object, it is very simple and offers sufficient generality such that integration with concurrent mapping and localizing can be achieved without major modifications. The performance and simplicity of the approach are demonstrated with the results produced by sets of experiments using a mobile robot equipped with sonar sensors.

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.270-274
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• 2007

In this paper, we present a Sound Source Localization (SSL) based GCC (Generalized Cross Correlation)-PHAT (Phase Transform) and new measurement method of angle with robot auditory system for a network-based intelligent service robot. The main goal of this paper is to analysis performance of TDOA and GCC-PHAT sound source localization method and new angle measurement method is compared. We use GCC-PHAT for measuring time delays between several microphones. And sound source location is calculated by using time delays and new measurement method of angle. The robot platform used in this work is wever-R2, which is a network-based intelligent service robot developed at Intelligent Robot Research Division in ETRI.

• The Journal of Korea Robotics Society
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• v.7 no.1
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• pp.45-56
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• 2012

This paper proposes how to improve the performance of CSS-based indoor localization system. CSS based localization utilizes signal flight time between anchors and tag to estimate distance. From the distances, the 3-dimensional position is calculated through trilateration. However the error in distance caused from multi-path effect transfers to the position error especially in indoor environment. This paper handles a problem of reducing error in raw distance information. And, we propose the new localization method by pattern matching instead of the conventional localization method based on trilateration that is affected heavily on multi-path error. The pattern matching method estimates the position by using the fact that the measured data of near positions possesses a high similarity. In order to gain better performance of localization, we use EKF(Extended Kalman Filter) to fuse the result of CSS based localization and robot model.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.92.3-92
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• 2002

A lot of localization algorithms have been developed in order to achieve autonomous navigation. However, most of localization algorithms are restricted to certain conditions. In this paper, Monte Carlo localization scheme with a map-matching algorithm is suggested as a robust localization method for the Public Service Robot to accomplish its tasks autonomously. Monte Carlo localization can be applied to local, global and kidnapping localization problems. A range image based measure function and a geometric pattern matching measure function are applied for map matching algorithm. This map matching method can be applied to both polygonal environments and un-polygonal environments and achieves...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.404-404
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• 2000

This paper presents a single vision-based sel(-localization method in an corridor environment. We use the Hough transform for finding parallel lines and vertical lines. And we use these cross points as feature points and it is calculated relative distance from mobile robot to these points. For matching environment map to feature points, searching window is defined and self-localization is performed by matching procedure. The result shows the suitability of this method by experiment.