• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.75-75
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• 2000

Recently, number of navigation system using GPS and other complementary sensors has been developed to offer high-position accuracy. In this paper, an integration of GPS and Dead-Reckoning, which consists of a fiber optical gyroscope and two high-precision wheel-motor encoders for a unmanned navigation system, is presented. The main objective of this integrated GPS/DR unmanned navigation system is to provide accurate position and heading navigation data continuously for autonomous mobile robot. We propose a method for increasing the accuracy of the estimated position of the mobile robot by its DR sensors, high-precision wheel-motor encoders and a fiber optical gyroscope. We used Kalman filter theory to combine GPS and DR measurements. The performance of GPS/DR navigation system is evaluated.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2009.04a
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• pp.157-162
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• 2009

Korea established a King Sejong Station in King George Island in 1988. With the establishment of a station, various researches such as geology, biology, geophysics and meteorites have been conducted. Surveying and mapping has been performed since 1990, however, the results had a large errors due to old GPS instrument and autonomous positioning with SA. In this study new GPS surveying was tried using a state-of-the-art GPS instrument and relative positioning.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.423-429
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• 2008

This study was performed to investigate the effects of inertial navigation system (INS) / global positioning system (GPS) sensor fusion for agricultural vehicle navigation. An extended Kalman filter algorithm was adopted for INS/GPS sensor fusion in an integrated mode, and the vehicle dynamic model was used instead of the navigation state error model. The INS/GPS system was consisted of a low-cost gyroscope, an odometer and a GPS receiver, and its performance was tested through computer simulations. When measurement noises of GPS receiver were 10, 1.0, 0.5, and 0.2 m ($1{\sigma}$), RMS position and heading errors of INS/GPS system at 5 m/s straight path were remarkably reduced with 10%, 35%, 40%, and 60% of those obtained from the GPS receiver, respectively. The decrease of position and heading errors by using INS/GPS rather than stand-alone GPS can provide more stable steering of agricultural equipments. Therefore, the low-cost INS/GPS system using the extended Kalman filter algorithm may enable the self-autonomous navigation to meet required performance like stable steering or more less position errors even in slow-speed operation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.194-194
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• 2000

In aviation navigation by GPS, requirements on availability and integrity must be absolutely satisfied for safety. Current study on accomplishing this integrity includes RAM(Receiver Autonomous Integrity Monitoring), checking integrity internally in GPS receiver itself. However RAIM techniques have be investigated and presented under assumption that there is included only one fault in measurements from GPS, In case of multiple fault, an interaction among bias errors sometimes results in decreasing the effect of multiple fault. This may make an exact fault detection and identification difficult, and study on mutiple fault RAIM focused on. This paper explains the reasons that techniques applied on single fault are not adequate to extend directly to two faults case and shows that RAIM solution on two fault may be given in revised parity space.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.1
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• pp.99-108
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• 2014

Recently, the interest in autonomous vehicle which is an aggregate of the automotive control technology is increasing. In particular, researches on the self-localization technology that is directly connected with stable driving of autonomous vehicle have been performed. Various dead reckoning technologies which are solutions for resolving the limitation of GPS have been introduced. However, the conventional dead reckoning technologies have two disadvantages to apply on the autonomous vehicle. First one is that the expensive sensors must be equipped additionally. The other one is that the accuracy of self-localization decreases caused by wheel slip when the vehicle's motion changed rapidly. Based on this background, in this paper, the wheel speed sensor which is equipped on most of vehicles was used and the dead reckoning algorithm considering wheel slip ratio was developed for autonomous vehicle. Finally, in order to evaluate the performance of developed algorithm, the various simulation were conducted and the results were compared with the conventional algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.529-536
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• 2004

Autonomous navigation of an indoor mobile robot using the global ultrasonic system is presented in this paper. Since the trajectory error of the dead-reckoning navigation grows with time and distance, the autonomous navigation of a mobile robot requires to localize the current position of the robot, so that to compensate the trajectory error. The global ultrasonic system consisting of four ultrasonic generators fixed at a priori known positions in the work space and two receivers on the mobile robot has the similar structure with the well-known satellite GPS(Global Positioning System), and it is useful for the self-localization of an indoor mobile robot. The EKF(Extended Kalman Filter) algorithm for the self-localization is proposed and the autonomous navigation based on the self-localization is verified by experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.617-624
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• 2010

This article describes the system architecture of KUVE (KIST Unmanned Vehicle Electric) and unmanned autonomous navigation of it in KIST. KUVE, which is an electric light-duty vehicle, is equipped with two laser range finders, a vision camera, a differential GPS system, an inertial measurement unit, odometers, and control computers for autonomous navigation. KUVE estimates and tracks the boundary of road such as curb and line using a laser range finder and a vision camera. It follows predetermined trajectory if there is no detectable boundary of road using the DGPS, IMU, and odometers. KUVE has over 80% of success rate of autonomous navigation in KIST.

• ETRI Journal
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• v.43 no.4
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• pp.580-593
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• 2021

When operating in confined spaces or near obstacles, collision-free path planning is an essential requirement for autonomous exploration in unknown environments. This study presents an autonomous exploration technique using a carefully designed collision-free local planner. Using LiDAR range measurements, a local end-point selection method is designed, and the path is generated from the current position to the selected end-point. The generated path showed the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The results consistently demonstrated the safety and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flights in environment with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial robot systems. In addition, our drone performed an autonomous mission in the tunnel circuit competition (Phase 1) of the DARPA Subterranean Challenge.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.20-25
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• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• 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.