• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.187-193
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• 2004

In this paper, we proposed the GPS position data correction method for autonomous land navigation using vanishing point property and a monocular vision system. Simulations are carried out over driving distances of approximately 60 km on the basis of realistic road data. On a straight road, the proposed method reduces GPS position error by at least 63% within 0.5 m. However, the average accuracy of the method is not presented, because it is difficult to estimate it on other than a straight road in variable conditions.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.508-510
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• 2004

In this paper, we proposed the GPS position data correction method for autonomous land navigation using vanishing point property and a monocular vision system. Simulations are carried out over driving distances of approximately 60 km on the basis of realistic road data. In straight road, the proposed method reduces GPS position error to minimum more than 63% and positioning errors within less than 0.5m are observed. However, the average accuracy of the method is not presented. because it is difficult to estimate it in curve road or other road environments.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.1
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• 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.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• Journal of Navigation and Port Research
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• v.38 no.2
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• pp.121-126
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• 2014

This paper suggests a new algorithm to provide low-cost GPS modules with DGPS service, which corrects the error vector in the already-calculated position by projecting range corrections to position domain using the observation matrix calculated from the satellite elevation and azimuth angle in the NMEA GPGSV data. The algorithm reduced the horizontal and vertical RMS error of U-blox LEA-5H module from 1.8m/5.8m to 1.0m/1.4m during the daytime. The algorithm has advantage in improving the performance of low-cost module to that of DGPS receiver by a software update without any correction in hardware, therefore it is expected to contribute to the vitalization of the future high-precision position service infrastructure by reducing the costumer cost and vender risk.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1338-1346
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• 1995

In this paper, we suggest the implementation of a DGPS system using two low cost commercial C/A code GPS modules and modems and its efficient operational techniques to provide DGPS service which guarantees the position accuracy of better than 10 meters for more users. The proposed DGPS system can be implemented easil at low cost because it needs a GPS module and a modem for each reference station and user. The reference station makes plans of the receiving schedule from the satellite set at each period and then provides the correction data for various satellite sets in a period. The main contribution of this paper is that users can utilize the correction data continuously and efficiently through the recursive least squares lattice filtering method. Experimental results show the position accuracy of better than 10 meters using the suggested DGPS system in almost real time.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.1
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• pp.16-25
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• 1997

This paper shows the development of the high accuracy vehicle positioning algorithm using the differential technique in vehicle tracking systems form the existing vehicle position which is acquired from the global positioning system (GPS). The control center receives the satellite ephemerise data and pseudorange correction from the reference station, and vehicle position from the moving vehicle. The pseudorange is calculated with the satellite position and the vehicle position, and corrected by pseudorange correction. Using this corrected pseudorange and kalman filter, more improved vehicle positioning data were obtained.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.37-46
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• 2016

The difference between definition time of GPS (Global Positioning System) position data and actual display time of car positions on a map could reduce the accuracy of car positions displayed in PND (Portable Navigation Device)-type CNS (Car Navigation System). Due to the time difference, the position of the car displayed on the map is not its current position, so an improved method to fix these problems is required. It is expected that a method that uses predicted future positionsto compensate for the delay caused by processing and display of the received GPS signals could mitigate these problems. Therefore, in this study an analysis was conducted to correct late processing problems of map positions by mapmatching using a Kalman filter with only GPS position data and a RRF (Road Reduction Filter) technique in a light-weight CNS. The effects on routing services are examined by analyzing differences that are decomposed into along and across the road elements relative to the direction of advancing car. The results indicate that it is possible to improve the positional accuracy in the along-the-road direction of a light-weight CNS device that uses only GPS position data, by applying a Kalman filter and RRF.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.237-248
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• 2020

GNSS has been evolving dramatically in recent years. There are currently 6 GNSS (4 GNSS, AND 2 RNSS) constellations, which are GPS (USA), GLONASS (Russia), BeiDou (China), Galileo (EU), QZSS (Japan), and IRNSS (India). The Number of navigation satellites is expected to be over 150 by 2020. As the number of both constellations and satellites used for the improvement of positioning performance, high accuracy, and robustness of precise positioning is more promising. However, a large amount of the correction messages is required to support the augmentation system for the available satellites of all the constellations. Since bandwidth for the correction messages is generally limited, sending or scheduling the correction messages might be a critical issue in the near future. In this study, we analyze the relationship between the size of the bandwidth and Real-Time Kinematics (RTK) performance. Multiple Signal Messages (MSM), the only Radio Technical Commission for Maritimes (RTCM) message that supports multi-constellation GNSS, has been used for this assessment. Instead of the conventional method that broadcasts all the messages at the same time, we assign the MSM broadcasting interval for each constellation in 5 seconds. An open sky static and dynamic test for this study was conducted on the roof of Sejong University. Our results show that the RTK fixed position accuracy is not affected by the 5-second interval corrections, but the ambiguity fixing rate is degraded for poor DOP cases when RTK correction are transmitted intermittently.