• Journal of Positioning, Navigation, and Timing
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• v.9 no.1
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• pp.23-31
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• 2020

It is essential to estimate the vehicle localization for an autonomous safety driving. In particular, since LIDAR provides precise scan data, many studies carried out to estimate the vehicle localization using LIDAR and pre-generated map. The road marking always exists on the road because of provides driving information. Therefore, it is often used for map information. In this paper, we propose to generate the Gaussian mixture map based on road-marking information and localization method using this map. Generally, the probability distributions map stores the single Gaussian distribution for each grid. However, single resolution probability distributions map cannot express complex shapes when grid resolution is large. In addition, when grid resolution is small, map size is bigger and process time is longer. Therefore, it is difficult to apply the road marking. On the other hand, Gaussian mixture distribution can effectively express the road marking by several probability distributions. In this paper, we generate Gaussian mixture map and perform vehicle localization using Gaussian mixture map. Localization performance is analyzed through the experimental result.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.24-30
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• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• ETRI Journal
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• v.36 no.6
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• pp.968-978
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• 2014

Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision-based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego-motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a $300m{\times}500m$ space.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.451-459
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• 2011

Fundamentally, there are 5 systems are needed for autonomous navigation of unmanned ground vehicle: Localization, environment perception, path planning, motion planning and vehicle control. Path planning and motion planning are accomplished based on result of the environment perception process. Thus, high reliability of localization and the environment perception will be a criterion that makes a judgment overall autonomous navigation. In this paper, via map matching using vehicle dynamic model and LIDAR sensors, replace high price localization system to new one, and have researched an algorithm that lead to robust autonomous navigation. Finally, all results are verified via actual unmanned ground vehicle tests.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.133-139
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• 2013

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.132-151
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• 2022

Vehicle localization is one of the core technologies for autonomous driving. Image-based localization provides location information efficiently, and various related studies have been conducted. However, the image-based localization methods using feature points or lane information has a limitation that positioning accuracy may be greatly affected by road and driving environments. In this study, we propose a line segment matching framework for accurate vehicle localization. The proposed framework consists of four steps: line segment extraction, merging, overlap area detection, and MSLD-based segment matching. The proposed framework stably performed line segment matching at a sufficient level for vehicle positioning regardless of vehicle speed, driving method, and surrounding environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.430-435
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• 2011

In this paper, a navigation technology of an unmanned vehicle using relative localization and magnetic guidance is proposed. Magnetic guidance system had been developed as a robust autonomous driving technology as long as magnetic fields on the path are detected. Otherwise, if magnetic fields were not detected due to some reasons, the vehicle could not drive. Therefore, in order to overcome the drawback, we propose that relative localization would be combined to magnetic guidance system. To validate the usefulness of the proposed method, a robotic vehicle was set up with the magnetic guidance system and the relative localization. In addition, the unmanned driving test was realized on the road without the magnetic fields so that the proposed method is verified by the experiment.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.1
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• pp.27-33
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• 2017

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.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.795-800
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• 2008

The paper presents research for the established experiment environment of multi vehicle robot, localization algorithm that is based on vehicle control, and path tracking. The established experiment environment consists of ultrasonic positioning system, vehicle robot, server and wireless module. Ultrasonic positioning system measures positioning for using ultrasonic sensor and generates many errors because of the influence of environment such as a reflection of wall. For a solution of this fact, localization algorithm is proposed to determine a location using vehicle kinematics and selection of a reliable location data. And path tracking algorithm is proposed to apply localization algorithm and LOS, finally, that algorithms are verified via simulation and experimental

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.108-114
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• 2015

Automated driving systems require a high level of performance regarding environmental perception, especially in urban environments. Today's on-board sensors such as radars or cameras do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support these systems. An accurate digital map is used as a powerful additional sensor. In this paper, we propose a new approach for vehicle localization using a lane map and a single-layer LiDAR. The maps are created beforehand using a highly accurate DGPS and a single-layer LiDAR. A pose estimation of the vehicle was derived from an iterative closest point (ICP) match of LiDAR's intensity data to the lane map, and the estimated pose was used as an observation inside a Kalmanfilter framework. The achieved accuracy of the proposed localization algorithm is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control.