• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2475-2478
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• 2002

This paper consider's the problem of exploring an unfamiliar environment in search of recognizable objects of visual landmarks. In order to extract and recognize them automatically, a feature map is constructed which records the set of features continually during a learning phase. The map contains photometric geometric, and metric information of each feature. Meanwhile, the localization algorithm can determine the position of the robot by extracting features and matching in the map. These procedures are implemented and tested using an AMR, and preliminary results are presented in this paper.

• 제어로봇시스템학회논문지
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• 제11권5호
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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.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.62-75
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• 2008

Vision-based simultaneous localization and map building using a single camera, while compelling in theory, have not until recently been considered extensive in the practical realm of the real world. In this paper, we propose a simple framework for the monocular SLAM of an indoor mobile robot using natural line features. Our focus in this paper is on presenting a novel approach for modeling the landmark before integration in monocular SLAM. We also discuss data association improvement in a particle filter approach by using the feature management scheme. In addition, we take constraints between features in the environment into account for reducing estimated errors and thereby improve performance. Our experimental results demonstrate the feasibility of the proposed SLAM algorithm in real-time.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1767-1768
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• 2007

This paper represents a map building system of Embedded Linux mobile robot. We propose a localization method which uses multiple sensors such as indoor GPS and encoder sensor for simultaneous map building system. In this paper we proposed a multiple sensor system for SLAM. For this, we developed a sensor based navigation algorithm and grid based map building algorithm under the Embedded Linux O.S. We proved this system's validity through field test

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.637-640
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• 2005

We propose a sound source localization method using the Head-Related-Transfer-Function (HRTF) to be implemented in a given platform. HRTFs contain not only the information regarding proper time delays but also phase and magnitude distortions due to diffraction and scattering by the shading object. Therefore, a set of HRTFs for any given platform provides a substantial amount of information as to the whereabouts of the source. In this study, we introduce new phase criterion in order to find the sound source location in accordance with the HRTF database empirically obtained in an anechoic chamber with the given platform. Using this criterion, we analyze the estimation performance of the proposed method in a household environment.

• Journal of Positioning, Navigation, and Timing
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• 제11권4호
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• pp.389-395
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• 2022

In order to ensure reliability the high-level automated driving such as Advanced Driver Assistance System (ADAS) and universal robot taxi provided by autonomous driving systems, the operation with high integrity must be generated within the defined Operation Design Domain (ODD). For this, the position and posture accuracy requirements of autonomous driving systems based on the safety driving requirements for autonomous vehicles and domestic road geometry standard are necessarily demanded. This paper presents localization requirements for safe road driving of autonomous ground vehicles based on the requirements of the positioning system installed on autonomous vehicle systems, the domestic road geometry standard and the dimensions of the vehicle to be designed. Based on this, 4 Protection Levels (PLs) such as longitudinal, lateral, vertical PLs, and attitude PL are calculated. The calculated results reveal that the PLs are more strict to urban roads than highways. The defined requirements can be used as a basis for guaranteeing the minimum reliability of the designed autonomous driving system on roads.

• 로봇학회논문지
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• 제17권3호
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• pp.334-338
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• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.

• 제어로봇시스템학회논문지
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• 제22권3호
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• pp.210-216
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• 2016

This paper presents a novel collision avoidance technique for mobile robots based on omni-directional vision simultaneous localization and mapping (SLAM). This method estimates the avoidance path and speed of a robot from the location of an obstacle, which can be detected using the Lucas-Kanade Optical Flow in images obtained through fish-eye cameras mounted on the robots. The conventional methods suggest avoidance paths by constructing an arbitrary force field around the obstacle found in the complete map obtained through the SLAM. Robots can also avoid obstacles by using the speed command based on the robot modeling and curved movement path of the robot. The recent research has been improved by optimizing the algorithm for the actual robot. However, research related to a robot using omni-directional vision SLAM to acquire around information at once has been comparatively less studied. The robot with the proposed algorithm avoids obstacles according to the estimated avoidance path based on the map obtained through an omni-directional vision SLAM using a fisheye image, and returns to the original path. In particular, it avoids the obstacles with various speed and direction using acceleration components based on motion information obtained by analyzing around the obstacles. The experimental results confirm the reliability of an avoidance algorithm through comparison between position obtained by the proposed algorithm and the real position collected while avoiding the obstacles.

• 로봇학회논문지
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• 제3권4호
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• pp.308-314
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• 2008

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze the result representation of SLAM using RBPF (RBPF-SLAM) when particle diversity is preserved. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. Thus, we propose several result representations and provide the analysis of the representations. For the analysis, estimation errors and their variances, and consistency of RBPF-SLAM are dealt in this study. According to the simulation results, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1820-1825
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• 2004

In this paper, we propose a humanoid active audition system which detects the direction of sound and performs speech recognition using just three microphones. Compared with previous researches, this system comprises simpler algorithm and better amplifier system having advantages to increase a detectible distance of sound signal in spite of simple circuit. In order to verify our system's performance, we install the proposed active audition system to the home service robot, called Hombot II, which has been developed at the KIST (Korea Institute of Science and Technology), thus we confirm excellent performance by experimental results