http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Precise Vehicle Localization Using 3D LIDAR and GPS/DR in Urban Environment
Im, Jun-Hyuck,Jee, Gyu-In The Institute of Positioning 2017 Journal of Positioning, Navigation, and Timing Vol.6 No.1
GPS provides the positioning solution in most areas of the world. However, the position error largely occurs in the urban area due to signal attenuation, signal blockage, and multipath. Although many studies have been carried out to solve this problem, a definite solution has not yet been proposed. Therefore, research is being conducted to solve the vehicle localization problem in the urban environment by converging sensors such as cameras and Light Detection and Ranging (LIDAR). In this paper, the precise vehicle localization using 3D LIDAR (Velodyne HDL-32E) is performed in the urban area. As there are many tall buildings in the urban area and the outer walls of urban buildings consist of planes generally perpendicular to the earth's surface, the outer wall of the building meets at a vertical corner and this vertical corner can be accurately extracted using 3D LIDAR. In this paper, we describe the vertical corner extraction method using 3D LIDAR and perform the precise localization by combining the extracted corner position and GPS/DR information. The driving test was carried out in an about 4.5 km-long section near Teheran-ro, Gangnam. The lateral and longitudinal RMS position errors were 0.146 m and 0.286 m, respectively and showed very accurate localization performance.
3D LIDAR Based Vehicle Localization Using Synthetic Reflectivity Map for Road and Wall in Tunnel
Im, Jun-Hyuck,Im, Sung-Hyuck,Song, Jong-Hwa,Jee, Gyu-In The Institute of Positioning 2017 Journal of Positioning, Navigation, and Timing Vol.6 No.4
The position of autonomous driving vehicle is basically acquired through the global positioning system (GPS). However, GPS signals cannot be received in tunnels. Due to this limitation, localization of autonomous driving vehicles can be made through sensors mounted on them. In particular, a 3D Light Detection and Ranging (LIDAR) system is used for longitudinal position error correction. Few feature points and structures that can be used for localization of vehicles are available in tunnels. Since lanes in the road are normally marked by solid line, it cannot be used to recognize a longitudinal position. In addition, only a small number of structures that are separated from the tunnel walls such as sign boards or jet fans are available. Thus, it is necessary to extract usable information from tunnels to recognize a longitudinal position. In this paper, fire hydrants and evacuation guide lights attached at both sides of tunnel walls were used to recognize a longitudinal position. These structures have highly distinctive reflectivity from the surrounding walls, which can be distinguished using LIDAR reflectivity data. Furthermore, reflectivity information of tunnel walls was fused with the road surface reflectivity map to generate a synthetic reflectivity map. When the synthetic reflectivity map was used, localization of vehicles was able through correlation matching with the local maps generated from the current LIDAR data. The experiments were conducted at an expressway including Maseong Tunnel (approximately 1.5 km long). The experiment results showed that the root mean square (RMS) position errors in lateral and longitudinal directions were 0.19 m and 0.35 m, respectively, exhibiting precise localization accuracy.
무인자동차의 경로점 주행 시 장애물 회피를 위한 경로생성 알고리즘
임준혁(Jun-Hyuck Im),유승환(Seung-Hwan You),지규인(Gyu-In Jee),이달호(Dal-Ho Lee) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.8
In this paper, an effective path generation algorithm for obstacle avoidance producing small amount of steering action as possible is proposed. The proposed path generation algorithm can reduce unnecessary steering because of the small lateral changes in generated waypoints when UGV (Unmanned Ground Vehicle) encounters obstacles during its waypoint navigation. To verify this, the proposed algorithm and A<SUP>*</SUP> algorithm are analyzed through the simulation. The proposed algorithm shows good performance in terms of lateral changes in the generated waypoint, steering changes of the vehicle while driving and execution speed of the algorithm. Especially, due to the fast execution speed of the algorithm, the obstacles that encounter suddenly in front of the vehicle within short range can be avoided. This algorithm consider the waypoint navigation only. Therefore, in certain situations, the algorithm may generate the wrong path. In this case, a general path generation algorithm like A<SUP>*</SUP> is used instead. However, these special cases happen very rare during the vehicle waypoint navigation, so the proposed algorithm can be applied to most of the waypoint navigation for the unmanned ground vehicle.
터널 내 소화기 표시등 검출을 이용한 3D LIDAR 기반 차량정밀측위
임준혁(Jun-Hyuck Im),임성혁(Sung-Hyuck Im),지규인(Gyu-In Jee) 제어로봇시스템학회 2018 제어·로봇·시스템학회 논문지 Vol.24 No.8
Vehicle localization is essential for autonomous driving. Basically, the position information of the autonomous vehicle is obtained from the Global Positioning System (GPS). More accurate localization can be performed by using maps and various sensors that are mounted on the autonomous vehicle. GPS receivers cannot receive GPS signals in tunnels, so dead reckoning (DR) is used for vehicle localization. However, the error from DR continuously accumulates. Therefore, this error must be corrected by using vehicle-mounted sensors, such as Light Detection and Ranging (LIDAR) and cameras. Tunnels have very specific shape information, which is usually an ellipse, and several emergency facilities exist in tunnels. Some facilities are separated from the tunnel wall, which can be detected by using 3D LIDAR. In particular, fire extinguisher lamps are periodically installed at intervals of 50 m, which can serve as good landmarks. First, the point cloud for the tunnel wall must be removed to effectively detect fire extinguisher lamps. This process can be easily conducted by using shape information. After this removal, we detect the fire extinguisher lamps. In this paper, we propose a 3D LIDAR-based vehicle-localization method that uses ellipse parameters and fire extinguisher lamps in road tunnels. These experiments are conducted at the Maseong tunnel in South Korea. The experimental results show that the root mean square (RMS) position errors in the lateral and longitudinal directions were 0.06 m and 0.23 m, respectively, exhibiting precise localization accuracy.
임준혁(Jun-Hyuck Im),임성혁(Sung-Hyuck Im),김우현(Woo-Hyun Kim),지규인(Gyu-In Jee) 제어로봇시스템학회 2010 제어·로봇·시스템학회 논문지 Vol.16 No.2
With GPS being the primary navigation system, Loran use is in steep decline. However, according to the final report of vulnerability assessment of the transportation infrastructure relying on the global positioning system prepared by the John A. Volpe National Transportation Systems Center, there are current attempts to enhance and re-popularize Loran as a GPS backup system through the characteristic of the ground based low frequency navigation system. To advance the Loran system such as Loran-C modernization and eLoran development, research is definitely needed in the field of Loran-C receiver signal processing as well as Loran-C signal design and the technology of a receiver. We have developed a set of Matlab tools, which implement a software Loran-C receiver that performs the receiver’s position determination through the following procedure. The procedure consists of receiving the Loran-C signal, cycle selection, calculation of the TDOA and range, and receiver’s position determination through the Least Square Method. We experiences the effect of an incorrect cycle selection and various error factors (ECD, ASF, sky wave, CRI, etc.) from the result of the Loran-C signal processing. It is apparent that researches which focus on the elimination and mitigation of various error factors need to be investigated on a software Loran-C receiver. These aspects will be explored in further work through the method such as PLL and Kalman filtering.
Lee, Jun Hyuck,Kim, Soo Young,Rho, Seong-Hwan,Im, Young Jun,Kim, Young Ran,Kim, Mun-Kyoung,Kang, Gil Bu,Rhee, Joon Haeng,Eom, Soo Hyun Korean Society for Molecular Biology 2005 Molecules and cells Vol.20 No.3
<P>Plasmid Achromobacter secretion (PAS) factor is a putative secretion factor that induces the secretion of periplasmic proteins. PAS factor from Vibrio vulnificus was crystallized at 294 K by the hanging drop vapor-diffusion method. It was isolated as a monomer during the purification procedures. The native crystal belongs to the F222 space group with unit cell parameters a=56.1, b=74.4, c=80.0 A, a=b=g=90 degrees. The crystal was soaked in cryoprotectant containing 1 M NaBr for 1 h for MAD phasing. The diffraction limit of the Br-MAD data set was 1.9 A using synchrotron X-ray irradiation at beam line BL-18B at the Photon Factory, Japan.</P>
도심에서 수직구조물 랜드마크를 이용한 3D LIDAR 기반 차량정밀측위
임준혁(Jun-Hyuck Im),임성혁(Sung-Hyuck Im),송종화(Jong-Hwa Song),지규인(Gyu-In Jee) 제어로봇시스템학회 2017 제어·로봇·시스템학회 논문지 Vol.23 No.12
Tall buildings in urban areas obstruct the GPS satellite signal reception, thus the GPS position accuracy can be very poor. However, buildings in urban areas can be used to generate a landmark map for precise vehicle localization. This map’s information can be represented in various forms. We focused on the outer wall of a building that was erected vertically from the ground and almost flat. Therefore, vertical corners that meet the vertical planes are present everywhere in urban areas. These corners can provide very good landmarks and can be extracted using LIDAR. Additionally, traffic signs can be used for localization. The traffic signs are present in the road and reflect light well. Therefore, traffic signs can be clearly distinguished from other structures using LIDAR reflectivity. In this paper, we used building’s vertical corner and traffic signs for vehicle localization in an urban area. In addition, we proposed a vertical structure map with information for vertical corners and traffic signs. The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.5 km and the maximum traveling speed was approximately 80 km/h. The lateral and longitudinal RMS position errors were 0.118 m and 0.231 m, respectively.