• Journal of Positioning, Navigation, and Timing
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• v.9 no.4
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• pp.387-395
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• 2020

Accuracy of an integrated Global Positioning System (GPS) / Inertial Navigation System (INS) relies heavily on the visibility of GPS satellites. Especially, its accuracy is dramatically degraded in urban canyon due to signal obstructions due to large structures. In this paper, we propose a new integrated positioning system that effectively combines INS, GPS, ultrasonic sensor, and barometer in GPS-denied environments. In the proposed system, the ultrasonic sensor provides velocity information along the forward direction of moving vehicle. The barometer output provides height information compensated for the pressure variation due to fast vehicle movements. To evaluate the performance of the proposed system, an experiment was carried out by mounting the proposed system on a test car. By the experiment result, it was confirmed that the proposed system bears good potential to maintain positioning accuracy in harsh urban environments.

• Journal of Advanced Navigation Technology
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• v.23 no.3
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• pp.228-236
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• 2019

Recently, multi-sensor integration techniques have been actively studied to obtain reliable and accurate navigation solution in GPS (Global Positioning System)-denied harsh environments such as urban canyons, tunnels, and underground roads. In this paper, we propose a low-cost ultrasonic-speedometer utilizing the characteristics of the ultrasonic propagation. An efficient integrated INS (inertial navigation system)/ultrasonic-speedometer navigation system is also proposed to improve the accuracy of positioning in GPS-denied environments. To evaluate the proposed system, car experiments with field-collected measurements were performed. By the experiment results, it was confirmed that the proposed INS/ultrasonic-speedometer system bounds the positioning error growth effectively even though GPS signal is blocked more than 10 seconds and a low-cost MEMS IMU (micro electro mechanical systems inertial measurement unit) is utilized.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.3
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• pp.258-266
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• 2014

We present a system for the real-time visual relative navigation of a fixed-wing unmanned aerial vehicle in a GPS-denied environment. An extended Kalman filter is used to construct a vision-aided navigation system by fusing the image processing results with barometer and inertial sensor measurements. Using a mean-shift object tracking algorithm, an onboard vision system provides pixel measurements to the navigation filter. The filter is slightly modified to deal with delayed measurements from the vision system. The image processing algorithm and the navigation filter are verified by flight tests. The results show that the proposed aerial system is able to maintain circling around a target without using GPS data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.15-18
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• 2023

UAV는 군사용을 처음 시작으로 근래에 취미용 드론의 급격한 성장과 더불어 최근 기후변화, 교통혼잡, 범죄 예방 등 여러 사회 문제 해결을 위한 드론의 필요성이 증가함에 따라 건설, 교통, 농업, 에너지, 엔터테인먼트 등 다양한 산업과 여러 사회 서비스로 그 필요성이 확대되고 있다. 본 연구는 이러한 사회적 흐름에 따라 인공지능 기술을 통한 드론의 활용성을 확대하고 GPS 수신이 안 되는 환경에서 딥러닝 객체 탐지 모델을 활용한 자율 착륙을 연구를 목표로 한다. GPS 신호는 실내와 같은 환경 혹은 지하, 교량 아래, 산속 등과 같은 곳에서는 수신이 어렵다. 이를 극복하고자 GPS 신호수신이 어려운 지역에서 GPS 수신기를 통해 받는 위치 정보 대신 드론에 장착된 카메라를 통해 전달받는 영상에서 착륙할 지점을 인식하고 카메라를 통해 받는 영상 정보만 이용하여 목표지점으로 하강하는 방식으로 자율 착륙을 유도한다. 딥러닝 중 경량화 모델을 활용하여 소형 드론에서 실시간으로 착륙 지점을 감지하기 위해 최적화 과정을 진행해 실시간 자율 착륙이 가능하게 하였다. 본 연구를 통해 드론의 착륙에 있어 GPS 수신기와 사람의 조종에 대한 의존도를 낮출 수 있을 것으로 기대한다.

• Korean Journal of Artificial Intelligence
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• v.12 no.3
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• pp.9-15
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• 2024

This paper addresses the critical need for quadcopters in GPS-denied indoor environments by proposing a novel attitude control mechanism that enables autonomous navigation without external guidance. Utilizing AR marker detection integrated with a dual PID controller algorithm, this system ensures accurate maneuvering and positioning of the quadcopter by compensating for the absence of GPS, a common limitation in indoor settings. This capability is paramount in environments where traditional navigation aids are ineffective, necessitating the use of quadcopters equipped with advanced sensors and control systems. The actual position and location of the quadcopter is achieved by AR marker detection technique with the image processing system. Moreover, in order to enhance the reliability of the attitude PID control, the dual closed loop control feedback PID control with dual update periods is suggested. With AR marker detection technique and autonomous attitude control, the proposed quadcopter system decreases the need of additional sensor and manual manipulation. The experimental results are demonstrated that the quadrotor's autonomous attitude control and operation with the dual closed loop control feedback PID controller with hierarchical (inner-loop and outer-loop) command update period is successfully performed under the non-GPS aided indoor environment and it enhanced the reliability of the attitude and the position PID controllers within 17 seconds. Therefore, it is concluded that the proposed attitude control mechanism is very suitable to GPS-denied indoor environments, which enables a quadcopter to autonomously navigate and hover without external guidance or control.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.125-129
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• 2020

In this paper, Zero velocity UPdaTe (ZUPT) is implemented on the navigation system of Remotely Piloted Aircraft for GNSS denied environment. RPA's navigation system suffers from lack or loss of satellite signal while maintenance or ground test inside a hangar. Although some of the hangars install GPS repeaters for indoor tests, the anti-jamming equipment with array antenna blocks the repeater signal regarding them as hostile jamming signal. With ZUPT, an aircraft navigation system can be tested free from the divergence of navigation solution without line-of-sight satellites. The designed ZUPT aided centralized Kalman Filter is implemented on the Embedded GPS&INS and simulated with Captive Flight Test data. The simulation result shows stable navigation solution without GNSS updates.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.2
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• pp.59-70
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• 2017

Since GPS signals are vulnerable to interference and obstruction, many alternate aiding systems have been proposed to integrate with an inertial navigation system. Among these alternate systems, the vision-aided method has become more attractive due to its benefits in weight, cost and power consumption. This paper proposes a loosely-coupled vision/INS integrated navigation method which can work in GPS-denied environments. The proposed method improves the navigation accuracy by correcting INS navigation and sensor errors using position and attitude outputs of a landmark based vision navigation system. Furthermore, it has advantage to provide redundant navigation output regardless of INS output. Computer simulations and the van tests have been carried out in order to show validity of the proposed method. The results show that the proposed method works well and gives reliable navigation outputs with better performance.

• International Journal of Ocean System Engineering
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• v.1 no.2
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• pp.89-94
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• 2011

Underwater localization is a crucial capability for reliable operation of various types of underwater vehicles including submarines and underwater robots. However, sea water is almost impermeable to high-frequency electromagnetic waves, and thus absolute position fixes from Global Positioning System (GPS) are not available in the water. The use of acoustic telemetry systems such as Long Baseline (LBL) is a practical option for underwater localization. However, this telemetry network system needs to be pre-deployed and its availability cannot always be assumed. This study focuses on demonstrating the validity of terrain-based localization techniques in a GPS-denied underwater environment. Since terrain-based localization leads to a nonlinear estimation problem, nonlinear filtering methods are required to be employed. The extended Kalman filter (EKF) which is a widely used nonlinear filtering algorithm often shows limited performance under large initial uncertainty. The feasibility of using a particle filter is investigated, which can improve the performance and reliability of the terrain-based localization.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.1-11
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• 2020

Detecting obstacles and generating a suitable path to avoid obstacles in real time is a prime mission requirement for UAVs. In areas, close to buildings and people, detecting obstacles in the path and estimating its own position (egomotion) in GPS degraded/denied environments are usually addressed with vision-based Simultaneous Localization and Mapping (SLAM) techniques. This presents possibilities and challenges for the feasible path generation with constraints of vehicle dynamics in the configuration space. In this paper, a near real-time feasible path is shown to be generated in the ORB-SLAM framework using a chain-based path planning approach in a force field with dynamic constraints on path length and minimum turn radius. The chain-based path plan approach generates a set of nodes which moves in a force field that permits modifications of path rapidly in real time as the reward function changes. This is different from the usual approach of generating potentials in the entire search space around UAV, instead a set of connected waypoints in a simulated chain. The popular ORB-SLAM, suited for real time approach is used for building the map of the environment and UAV position and the UAV path is then generated continuously in the shortest time to navigate to the goal position. The principal contribution are (a) Chain-based path planning approach with built in obstacle avoidance in conjunction with ORB-SLAM for the first time, (b) Generation of path with minimum overheads and (c) Implementation in near real time.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.