Gyu-In Jee

Low Earth Orbit (LEO)-based positioning, navigation, and timing (PNT) systems have attracted growing interest owing to their strong transmission power and rapid Precise Point Positioning (PPP) convergence. A key challenge in realizing such systems is constructing a pseudo-noise (PN) code family large enough to accommodate hundreds of satellites while maintaining competitive correlation performance. In this study, we propose the Concatenated Weil (C.W.) family, an extended Weil construction that concatenates two Weil sequences whose prime periods sum to the target code length, motivated by the Goldbach conjecture. A two-stage search—auto-correlation screening followed by cross-correlation screening—identifies 608 candidate codes satisfying the correlation thresholds of established modernized Radio Navigation Satellite System (RNSS) families. A subsequent spectral refinement based on the minimum-to-average ratio of Power Spectral Density (PSD) removes codes with deep spectral nulls, yielding a final family of 588 balanced codes. Benchmarking against modernized RNSS PN families demonstrates that the proposed family achieves the largest family size while maintaining comparable correlation performance, thereby providing a viable PN solution for large-scale LEO-PNT constellations.

Low Earth Orbit (LEO)-based positioning, navigation, and timing (PNT) systems have attracted growing interest owing to their strong transmission power and rapid Precise Point Positioning (PPP) convergence. A key challenge in realizing such systems is constructing a pseudo noise (PN) code family large enough to accommodate hundreds of satellites while maintaining competitive correlation performance. In this study, we propose the Concatenated Weil (C.W.) family, an extended Weil construction that concatenates two Weil sequences whose prime periods sum to the target code length, motivated by the Goldbach conjecture. A two-stage search---auto-correlation screening followed by cross-correlation screening---identifies 608 candidate codes satisfying the correlation thresholds of established modernized Radio Navigation Satellite System (RNSS) families. A subsequent spectral refinement based on the minimum-to-average Power Spectral Density (PSD) ratio removes codes with deep spectral nulls, yielding a final family of 588 balanced codes. Benchmarking against modernized RNSS PN families demonstrates that the proposed family achieves the largest family size while maintaining comparable correlation performance, thereby providing a viable PN solution for large-scale LEO-PNT constellations.

In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community.

Vehicle localization in highway tunnels is challenging for autonomous vehicle navigation. The drift error increases because GPS signals from satellites cannot be received inside a highway tunnel. Therefore, map-aided localization is essential for correcting the drift error. However, the environment around the tunnel is composed mainly of elliptical walls, and there are few unique feature points for map matching, unlike the case outdoors. In this paper, we propose a tunnel facility–based precise vehicle localization method for highway tunnels using 3D LIDAR. For vehicle localization, a point landmark map storing the center points of the tunnel facilities and a probability distribution map storing the probability distributions of the lane markings is used. In the proposed method, the tunnel facility points are extracted using 3D LIDAR, and position estimation is conducted using an extended Kalman filter-based navigation filter. The proposed localization algorithm was verified through experiments using actual highway driving data. The experimental results verify that tunnel facility–based vehicle localization yields precise results in various situations.

We propose a free-resolution probability distributions map (FRPDM) and an FRPDM-based precise vehicle localization method using 3D light detection and ranging (LIDAR). An FRPDM is generated by Gaussian mixture modeling, based on road markings and vertical structure point cloud. Unlike single resolution or multi-resolution probability distribution maps, in the case of the FRPDM, the resolution is not fixed and the object can be represented by various sizes of probability distributions. Thus, the shape of the object can be represented efficiently. Therefore, the map size is very small (61 KB/km) because the object is effectively represented by a small number of probability distributions. Based on the generated FRPDM, point-to-probability distribution scan matching and feature-point matching were performed to obtain the measurements, and the position and heading of the vehicle were derived using an extended Kalman filter-based navigation filter. The experimental area is the Gangnam area of Seoul, South Korea, which has many buildings around the road. The root mean square (RMS) position errors for the lateral and longitudinal directions were 0.057 m and 0.178 m, respectively, and the RMS heading error was 0.281°.

Road markings are always present on roads to guide and control traffic. Therefore, they can be used at any time for vehicle localization. Moreover, they can be easily extracted by using light detection and ranging (LIDAR) intensity because they are brightly colored. We propose a vehicle localization method using a 2D road marking grid map. The grid map inserts the map information into the grid directly. Thus, an additional process (such as line detection) is not required and there is no problem due to false detection. We obtained road marking using a 3D LIDAR (Velodyne HDL-32E) and binarized this information to store in the map. Thus, we could reduce the map size significantly. In the previous research, the road marking grid map was used only for position estimation. However, we propose a position-and-heading estimation algorithm using the binary road marking grid map. Accordingly, we derive more precise position estimation results. Moreover, position reliability is an important factor for vehicle localization. Autonomous vehicles may cause accidents if they cannot maintain their lane momentarily. Therefore, we propose an algorithm for evaluating map matching results. Consequently, we can use only reliable matching results and increase position reliability. The experiment was conducted in Gangnam, Seoul, where GPS error occurs largely. In the experimental results, the lateral root mean square (RMS) error was 0.05 m and longitudinal RMS error was 0.08 m. Further, we obtained a position error of less than 50 cm in both lateral and longitudinal directions with a 99% confidence level.

An Extended Line Map (ELM)-based precise vehicle localization method is proposed in this paper, and is implemented using 3D Light Detection and Ranging (LIDAR). A binary occupancy grid map in which grids for road marking or vertical structures have a value of 1 and the rest have a value of 0 was created using the reflectivity and distance data of the 3D LIDAR. From the map, lines were detected using a Hough transform. After the detected lines were converted into the node and link forms, they were stored as a map. This map is called an extended line map, of which data size is extremely small (134 KB/km). The ELM-based localization is performed through correlation matching. The ELM is converted back into an occupancy grid map and matched to the map generated using the current 3D LIDAR. In this instance, a Fast Fourier Transform (FFT) was applied as the correlation matching method, and the matching time was approximately 78 ms (based on MATLAB). The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.2 km, and the maximum traveling speed was approximately 80 km/h. As a result of localization, the root mean square (RMS) position errors for the lateral and longitudinal directions were 0.136 m and 0.223 m, respectively.

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.