• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2784-2786
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• 2000

Mobile robots include control modules for autonomous obstacle avoidance and navigation. They are range modules to detect and avoid obstacles. motor control modules to operate two wheels. and encoder modules for localization. There is needed an appropriate controller for each modules. In this paper. a control system. including 18 channels for Sonar sensors. 4 channels for PWM modules. and 4 channels for encoder modules. is proposed using TMS320C32 DSP adopted with CAN. The board communicates with other modules by CAN. so that mobile robots can perform several tasks in real time. So we can realize on autonomous mobile robot with basic functions such as obstacle avoidance by using the developed controller. Especially. this controller has 100 msec scan time for 16 sonar sensors and can detect closer objects comparing with standard sonar sensors.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1021-1026
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• 2016

The graph-based SLAM (Simultaneous Localization and Mapping) approach has been gaining much attention in SLAM research recently thanks to its ability to provide better maps and full trajectory estimations when compared to the filtering-based SLAM approach. Even though graph-based SLAM requires batch processing causing it to be computationally heavy, recent advancements in optimization and computing power enable it to run fast enough to be used in real-time. However, data association problems still require large amount of computation when building a pose graph. For example, to find loop closures it is necessary to consider the whole history of the robot trajectory and sensor data within the confident range. As a pose graph grows, the number of candidates to be searched also grows. It makes searching the loop closures a bottleneck when solving the SLAM problem. Our approach to alleviate this bottleneck is to sample a limited number of pose nodes in which loop closures are searched. We propose a heuristic for sampling pose nodes that are most advantageous to closing loops by providing a way of ranking pose nodes in order of usefulness for closing loops.

• Journal of Navigation and Port Research
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• v.35 no.2
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• pp.121-130
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• 2011

The localization of vehicle is an important part of an unmanned vehicle control problem. Pseudolite ultrasonic system(PUS) is the method to find an absolute position with a high accuracy by using ultrasonic sensor. And Gyro is the inertial sensor to measure yaw angle of vehicle. PUS can be able to estimate the position of mobile robot precisely, in which errors are not accumulated. And Gyro is a more faster measure method than PUS. In this paper, we suggest a more accuracy method of calculating PUS which is numerical analysis approach named Newtonian method. And also propose the fusion method to increase the accuracy of estimated angle on moving vehicle by using PUS and Gyro integrated system by Kalman filtering. To control the 4WS unmanned vehicle, the trajectory following algorithm is suggested. And the new concept arbitration of goal controller is suggested. This method considers the desirability function of vehicle state. Finally, the performances of Newtonian method and designed controller were verified from the experimental results with the 4WS vehicle scaled 1/10.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.94-96
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• 2021

In this paper, we develop a new navigation algorithm for industrial mobile robots to arrive at the destination in unknown environment. To achieve this, we suggest a navigation algorithm that combines Dynamic Window Approach (DWA) and Dijkstra path planning algorithm. We compare Local Dynamic Window Approach (LDWA), Global Dynamic Window Approach(GDWA), Rapidly-exploring Random Tree (RRT) Algorithm. The navigation algorithm using Dijkstra algorithm combined with LDWA and GDWA makes mobile robots to reach the destination. and obstacles faced during the path planning process of LDWA and GDWA. Then, we compare on time taken to arrive at the destination, obstacle avoidance and computation complexity of each algorithm. To overcome the limitation, we seek ways to use the optimized navigation algorithm for industrial use.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.775-778
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• 2007

로봇의 응용과 활용분야는 현 산업의 주요 이슈가 되고 있다. 현재 싱글로봇의 효율적인 운영을 넘어 넓은 공간에서 중복적인 공간 탐색을 최소화하기 위한 멀티 로봇 운영 기법은 중요한 연구 주제 중에 하나로 부각되고 있다. 멀티 로봇을 효율적으로 운영하기 위해서는 멀티 로봇 시스템의 각 싱글 로봇의 움직임을 파악하여 효율적으로 업무를 할당 할 수 있는 관리체계가 필요하다. 멀티 로봇의 업무 할당과 중복 탐색 최소화를 위해 본 논문에서는 중앙 서버와 RFID 시스템을 이용한 청소 멀티 로봇 운영 기법을 제안한다. 제안한 시스템은 로봇의 localization, navigation 및 mapping을 효율적으로 수행하기 위해 RFID를 활용하고 최적의 청소 공간 할당을 위하여 중앙 서버가 멀티 로봇을 효율적으로 관리한다. 청소 멀티 로봇 시스템에서는 싱글 로봇과 비교하여 효율적인 로봇의 운영을 보장할 뿐만 아니라 각 싱글 로봇의 상태와 주변 상태를 고려한 fault-tolerance를 제공함으로써 로봇 운영의 신뢰성을 보장할 수 있다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.64-73
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• 2012

In this paper, a new land preview system using laser range finder based on heave estimation algorithm is proposed. The proposed land preview system is an equipment which measures the shape of forward topography for autonomous vehicle. To implement this land preview system, the laser range finder is generally used because of its wide measuring range and robustness under various environmental condition. Then the current location of the vehicle has to be known to generate the shape of forward topography and sensors based on acceleration such as IMU and accelerometer are generally utilized to measure heave motion in the conventional land preview system. However the drawback to these sensors is that they are too expensive for low-cost vehicle such as mobile robot and their measurement error is increased for mobile robot with abrupt acceleration. In order to overcome this drawback, an algorithm that estimates heave motion using the information of odometer and previously measured topography is proposed in this paper. The proposed land preview system based on the heave estimation algorithm is verified through simulation and experiments for various terrain using a simulator and a real system.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.132-139
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• 2015

Visual SLAM(Simultaneous Localization And Mapping) has been used widely to estimate a mobile robot's location. Visual SLAM estimates relative motions with static visual features over image sequence. Because visual SLAM methods assume generally static features in the environment, we cannot obtain precise results in dynamic situation including many moving objects: cars and human beings. This paper presents a stereo vision based SLAM method in dynamic environment. First, we extract disparity map with stereo vision and compute optical flow. We then compute disparity change that is the estimated flow field between stereo views. After examining the disparity change value, we detect ROIs(Region Of Interest) in disparity space to determine dynamic scene objects. In indoor environment, many structural planes like walls may be determined as false dynamic elements. To solve this problem, we segment the scene into planar structure. More specifically, disparity values by the stereo vision are projected to X-Z plane and we employ Hough transform to determine planes. In final step, we remove ROIs nearby the walls and discriminate static scene elements in indoor environment. The experimental results show that the proposed method can obtain stable performance in dynamic environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.259-264
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• 2014

This paper is research paper on the MPD(Magnet Position Device) for the outdoor MGV(Magnet /Magnet Gyro Guidance Vehicle). Usually, MGV is used in indoor environment because of a measurement height of the magnet position device. CMPD(Commercial magnet position device) has 30 mm measurement height, so this is suitable structure in indoor environment like to a flat surface. Outdoor environment is an uneven and irregular, So Outdoor MGV must has a suspension. But CMPD is unsuitable for outdoor environment because of a collision with a surface caused by suspension. Thus, measurement height of the outdoor MPD is positively necessary more than 100 mm. So, we suggest the outdoor MPD using analog magnet hall sensor, moving average filter and Characteristic(rate of the magnet hall sensor) function of the localization. Result of the experiments, the proposed Magnet Position Device for the outdoor MGV has localization accuracy 4.31 mm, measurement height 150 mm and width 150 mm and is efficient more than CMPD.