• ETRI Journal
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• v.28 no.6
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• pp.761-769
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• 2006

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-to-noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple-source scenarios.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.206-212
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• 2010

In this paper, we introduce the concept of robotic zigbee network as a necessary network to provide an application service of robot in the ubiquitous environment and propose an application scenario using the concept of robot Zigbee network. We have performed experiments on the network connection and data transmission which are basic of proposed an application scenario. Through the result of the experiments, we provide basis for development of robot localization and tracking algorithm which minimizes the localization error using robot Zigbee network in the future.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.457-462
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• 2005

An empirical way of a covariance matrix which expresses the odometry uncertainty of mobile robots is proposed. This method utilizes PC-method which removes systematic errors of odometry. Once the systematic errors are removed, the odometry error can be modeled using the Gaussian probability distribution, and the parameters of the distribution can be represented by the covariance matrix. Experimental results show that the method yields $5{\%}$ and $2.3{\%}$ offset for the synchro and differential drive robots.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.725-730
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• 2012

In this paper, we propose the localization algorithm for the cooperative behavior of the swarm robots based on WSN (Wireless Sensor Network). The proposed method is as follows: First, we measure positions of the L-bot (Leader robot) and F-bots (Follower robots) by using the APIT (Approximate Point In Triangle) and the RSSI (Received Signal Strength Indication). Second, we measure relative positions of the F-bots against the pre-measured position of the L-bot by using trilateration. Then, to revise a position error caused by noise of the wireless signal, we use the particle filter. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• The Magazine of the IEIE
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• v.39 no.2
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• pp.45-54
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• 2012

Object detection is a fundamental task for many high-level computer vision applications such as image retrieval, scene understanding, activity recognition, visual surveillance and many others. Although object detection is one of the most popular problems in computer vision and various algorithms have been proposed thus far, it is also notoriously difficult, mainly due to lack of proper models for object representation, that handle large variations of object structure and appearance. In this article, we review a branch of object detection algorithms based on Support Vector Machines (SVMs), a well-known max-margin technique to minimize classification error. We introduce a few variations of SVMs-Structural SVMs and Latent SVMs-and discuss their applications to object detection and localization.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.353-358
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• 2022

Due to the limited field of view of the pinhole camera, there is a lack of stability and accuracy in camera pose estimation applications such as visual SLAM. Nowadays, multiple-camera setups and large field of cameras are used to solve such issues. However, a multiple-camera system increases the computation complexity of the algorithm. Therefore, in multiple camera-assisted visual simultaneous localization and mapping (vSLAM) the multi-view tracking algorithm is proposed that can be used to balance the budget of the features in tracking and local mapping. The proposed algorithm is based on PanoSLAM architecture with a panoramic camera model. To avoid the scale issue 3D LiDAR is fused with omnidirectional camera setup. The depth is directly estimated from 3D LiDAR and the remaining features are triangulated from pose information. To validate the method, we collected a dataset from the outdoor environment and performed extensive experiments. The accuracy was measured by the absolute trajectory error which shows comparable robustness in various environments.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.300-306
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• 2023

A hull-cleaning robot sticks to the surface of a vessel and moves for efficient cleaning. Precise path planning and tracking using the current position is crucial. Many robots rely on the INS algorithm, but errors accumulate. To fix this, GPS, sonar, and USBL are used, though with limitations. Selecting suitable sensors for the surface operation and accurate positioning algorithm are vital. In this study, we developed a robot position estimation algorithm using the structure of a ship. Problems that arise when expanding the 2D position estimation algorithm used in existing wall structures to 3D were evaluated and methods for solving them were proposed. In addition, we aimed to improve performance by deriving singularities that exist in the robot path and proposing an error correction algorithm based on the singularities.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.4
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• pp.447-455
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• 2018

If a hidden enemy is shooting, there is a threat against soldiers in recent conflicts. This paper aims to improve the localization of a muzzle using microphone array. Gunshot noise can provide information about the location of muzzle with two signals, the muzzle blast from the gun barrel and the projectile sound from the bullet. Two signals arrive to the microphone array with different arrival time and angle. If the arrival angles of the two signals are estimated, distance between sniper location and the microphone array can be calculated by using geometric principles. This method was established in 2003 by Pare. But this method has a limitation that it cannot calculate the distance when the arrival angles of the two signals are same. Also it has an error when the angle difference of arrival is small. In order to overcome this limitation, a new method is proposed that uses the change of characteristic of the projectile sound with respect to vertical distance from the trajectory. The proposed method estimates the distance correctly when the arrival angle of two signals are same, and when the angle difference between two signals is increased, the estimation error increases with respect to the angle. Therefore these two methods can be selected according to the angle difference between two signals to estimate the distance of the muzzle. Below the threshold of the angle difference, the proposed method can be used to estimate distance with smaller error than the existing method. This was demonstrated by shooting tests using actual sniper rifles.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1167-1175
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• 2010

This paper presents the use of the inertial measurement unit information and the infrared sector information for getting the position of an object. Travel distance is usually calculated from the double integration of the accelerometer output with respect to time; however, the accumulated errors due to the drift are inevitable. The orientation change of the accelerometer also causes error because the gravity is added to the measured acceleration. Unless three axis orientations are completely identified, the accelerometer alone does not provide correct acceleration for estimating the travel distance. We propose a way of minimizing the error due to the change of the orientation. In order to reduce the accumulated error, the infrared sector information is fused with the inertial measurement unit information. Infrared sector information has highly deterministic characteristics, different from RFID. By putting several infrared emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Infrared light based sector information tells the sector the object is in, but the size of the uncertainty is too large if only the sector information is used. This paper presents an algorithm which combines both the inertial measurement unit information and the sector information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed infrared light based sector and the proposed algorithm are verified from the experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.141-154
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• 2013

The technology of precise time-synchronization between signal receive devices for separation distance operation can be a key point for the technology with TDoA-based system. We propose a new method for the higher accuracy of system's time-synchronization in this paper, which uses OCXO and DPLL with high accuracy to achieve phase synchronization at 1 pps (pulse per second) of signal. And the method receive time value from a GPS satellite. Essentially, the performance of GPS with high accuracy refers to long-term frequency stability for its reliability. As per the characteristic, as the GPS timing signals are synchronized continuously, the accuracy of time-synchronization gets improved proportionally. Therefore, if the time synchronization is accomplished, the accuracy of the synchronization can be up to 0.001 ppb (part per billion). Through the improved accuracy of the time-synchronization, the measurement error of TDOA-based location detection technology is evaluated. Consequently, we verify that TDoA-based location measurement error can be greatly improved via using the improved method for time-synchronization error.