• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.228-236
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• 1999

In this paper, we propose visual-based self-localization and obstacle detection algorithms for indoor mobile robots. The algorithms do not require calibration, and can be worked with only single image by using the projective invariant relationship between natural landmarks. We predefine a risk zone without obstacles for a robot, and update the image of the risk zone, which will be used to detect obstacles inside the zone by comparing the averaging image with the current image of a new risk zone. The positions of the robot and the obstacles are determined by relative positioning. The method does not require the prior information for positioning robot. The robustness and feasibility of our algorithms have been demonstrated through experiments in hallway environments.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.709-715
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• 2010

This paper represents a map building and localization system for mobile robot. Map building and navigation is a complex problem because map integrity cannot be sustained by odometry alone due to errors introduced by wheel slippage, distortion and simple linealized odometry equation. For accurate localization, we propose sensor fusion system using encoder sensor and indoor GPS module as relative sensor and absolute sensor, respectively. To build a map, we developed a sensor based navigation algorithm and grid based map building algorithm based on Embedded Linux O.S. A wall following decision engine like an expert system was proposed for map building navigation. We proved this system's validity through field test.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.50-59
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• 2014

This paper proposes a method of localization of vehicle especially the horizontal position for the purpose of recognizing the driving lane. Through tracking road signs, the relative position between the vehicle and the sign is calculated and the absolute position is obtained using the known information from the regulation for installation. The proposed method uses Kalman filter for road sign tracking and analyzes the motion using the pinhole camera model. In order to classify the road sign, ORB(Oriented fast and Rotated BRIEF) features from the input image and DB are matched. From the absolute position of the vehicle, the driving lane is recognized. The Experiments are performed on videos from the highway driving and the results shows that the proposed method is able to compensate the common GPS localization errors.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.208-215
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• 2013

Early iterations of the existing Global Positioning System (GPS)-based or radio lateration technique-based vehicle localization algorithms suffer from flip ambiguities, forged relative location information and location information exchange overhead, which affect the subsequent iterations. This, in turn, results in an erroneous neighbor-vehicle map. This paper proposes an extended information overlap measure (EIOM) algorithm to reduce the flip error rates by exchanging the neighbor-vehicle presence features in binary information. This algorithm shifts and associates three pieces of information in the Moore neighborhood format: 1) feature information of the neighboring vehicles from a vision-based environment sensor system; 2) cardinal locations of the neighboring vehicles in its Moore neighborhood; and 3) identification information (MAC/IP addresses). Simulations were conducted for multi-lane highway scenarios to compare the proposed algorithm with the existing algorithm. The results showed that the flip error rates were reduced by up to 50%.

• Smart Structures and Systems
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• v.20 no.4
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• pp.397-413
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• 2017

Precast concrete (PC) members are currently being employed for general construction or partial replacement to reduce construction period. As assembly work in PC construction requires connecting PC members accurately, measuring the 6-DOF (degree of freedom) relative displacement is essential. Multiple planar markers and camera-based displacement measurement systems can monitor the 6-DOF relative displacement of PC members. Conventional methods, such as direct linear transformation (DLT) for homography estimation, which are applied to calculate the 6-DOF relative displacement between the camera and marker, have several major problems. One of the problems is that when the marker is partially hidden, the DLT method cannot be applied to calculate the 6-DOF relative displacement. In addition, when the images of markers are blurred, error increases with the DLT method which is employed for its estimation. To solve these problems, a hybrid method, which combines the advantages of the DLT and MCL (Monte Carlo localization) methods, is proposed. The method evaluates the 6-DOF relative displacement more accurately compared to when either the DLT or MCL is used alone. Each subsystem captures an image of a marker and extracts its subpixel coordinates, and then the data are transferred to a main system via a wireless communication network. In the main system, the data from each subsystem are used for 3D visualization. Thereafter, the real-time movements of the PC members are displayed on a tablet PC. To prove the feasibility, the hybrid method is compared with the DLT method and MCL in real experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.362-369
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• 2012

Recently, a target detection based on a distributed sensor network has been much studied in active sonar. Zhou et al. proposed a target localization method using line fitting based on a distributed sensor network which consists of low complexity sensors that only report binary detection results. This method has three advantages relative to ML estimator. First, there is no need to estimate propagation model parameters. Second, the computation is simple. Third, it only use sensors with "detection", which implies less data to be collected by data processing center. However, this method has larger target localization error than the ML estimator. In this paper, a target localization method which modifies Zhou's method is proposed for reducing the localization error. The modified method shows the performance improvement that the target localization error is reduced by 40.7% to Zhou's method in the point of RMSE.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.357-364
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• 2020

Indoor localization using an artificial marker plays a key role for a robot to be used in a service environment. A number of researchers have predefined the positions of markers and attached them to the positions in order to reduce the error of the localization method. However, it is practically impossible to attach a marker to the predetermined position accurately. In order to visualize the position of an object in the environment based on the marker attached to them, it is necessary to consider a change of marker's position or the addition of a marker because of moving the existed object or adding a new object. In this paper, we studied the method to estimate the artificial marker's global position for the visualization of environment. The system calculates the relative distance from a reference marker to others repeatedly to estimate the marker's position. When the marker's position is changed or new markers are added, our system can recognize the changed situation of the markers. To verify the proposed system, we attached 12 markers at regular intervals on the ceiling and compared the estimation result of the proposed method and the actual distance. In addition, we compared the estimation result when changing the position of an existing marker or adding a new marker.

• The Journal of Korea Robotics Society
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• v.11 no.3
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• pp.127-132
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• 2016

For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.586-591
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• 2010

In this paper, we propose the localization method of mobile robot using SURF(Speeded-Up Robust Features) and Particle filter. The proposed method is as follows: First, we seek the Landmark from the obtained image using SURF in order to find the first rigorous position of mobile robot. Second, we obtain the distance from obstacles using ultrasonic sensors in order to create the relative position of mobile robot. And then, we estimate the localization of mobile robot using Particle filter about movement of mobile robot. Finally, we show the feasibility of the proposed method through some experiments.