Joonhoo Lim

Adaptive GPS/INS integration for relative navigation

Abstract Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.

Feasibility of Integrated GNSS/OBD-II/IMU as a Prerequisite for Virtual Reality

To build accurate GIS (Geographical Information System) in urban area for advance virtual reality, accurate positioning is one of most important prerequisites. For this purpose, GNSS (Global Navigation Satellite System) is attractive since it can provide absolute coordinates to any object located on earth. However, accurate positioning is not a simple problem since any single GNSS cannot provide sufficient the number of visible satellites in urban area. To improve positioning availability in urban area for advanced virtual reality applications, this paper proposes a hybrid positioning method integrating a GPS/BeiDou receiver, an OBD-II (On-Board Diagnostics-II) device, and a MEMS IMU (Micro Electro Mechanical Systems-Inertial Measurement Unit). By an experiment result with field-collected actual measurements, the feasibility of the proposed method is evaluated.

Land vehicle positioning in urban area by integrated GPS/BeiDou/OBD-II/MEMS IMU

This paper proposes an efficient hybrid positioning method for land vehicles in urban area. As widely known, any single satellite positioning system suffers from the lack of satellite visibility in urban area. To improve positioning availability in urban area, the proposed method integrates a GPS/BeiDou receiver, an OBD-II (On-Board Diagnostics-II) device, and a MEMS IMU (Micro Electro Mechanical Systems-Inertial Measurement Unit). The proposed method corrects the inertial velocity by the speed provided by the OBD-II device. To evaluate the feasibility of the proposed method, an experiment was performed in a deep urban environment. By the experiment result demonstrates that positioning availability can be improved dramatically in deep urban area by integrating different sensors.

Estimation of Relative Distance from Surrounding Buildings by a Vision Sensor in Urban Area

The existing GNSS (Global Navigation Satellite System) has the weakness in availability and continuity for the vehicles in urban area since the number of visible satellites decreases abruptly due to large buildings and other structures. In this paper, a new method is proposed to estimate the relative lateral distance of a vehicle from surrounding buildings by a vision sensor to supplement the shortage of satellite-based positioning. In the proposed method, it is assumed that a vehicle, equipped with a vision sensor and a low-cost inertial measurement unit, is moving along a straight road segment in urban area. The common feature points contained in the two successive image frames from the single vision sensor are extracted. Based on the common feature points, the lateral depth information from buildings is estimated and utilized to improve availability and continuity of position solutions. To verify the conceptual feasibility of the proposed method, a simple experiment was performed.

Estimation of Angular Acceleration By a Monocular Vision Sensor

Recently, monitoring of two-body ground vehicles carrying extremely hazardous materials has been considered as one of the most important national issues. This issue induces large cost in terms of national economy and social benefit. To monitor and counteract accidents promptly, an efficient methodology is required. For accident monitoring, GPS can be utilized in most cases. However, it is widely known that GPS cannot provide sufficient continuity in urban cannons and tunnels. To complement the weakness of GPS, this paper proposes an accident monitoring method based on a monocular vision sensor. The proposed method estimates angular acceleration from a sequence of image frames captured by a monocular vision sensor. The possibility of using angular acceleration is investigated to determine the occurrence of accidents such as jackknifing and rollover. By an experiment based on actual measurements, the feasibility of the proposed method is evaluated.

An Implementation Study on a Low-cost High-accuracy Position Monitoring System for Unmanned Flying Robots

Abstract: Recently, UFRs (Unmanned Flying Robots) have begun to be utilized in various areas for civilian and military applications. Due to this increased utilization, accidents involving UFRsare also increasing. To prevent or monitor accidents caused by UFRs, high-accuracy positioning information is one of the most important technical elements. This paper proposes an efficient UFR monitoring system which provides accurate UFR positioning information with low-cost onboard elements; a small ARM module based on an embedded Linux operating system, a low-cost single frequency GPS receiver with a cheap patch antenna, and a versatile wireless network interface module. The ground monitoring system employs a dual frequency GPS receiver to generate exact UFR coordinates with cm-level accuracy. By processing the UFR measurements based on the Inverse RTK (Real Time Kinematic) method, the ground monitoring system determines the cm-level accurate coordinates of the UFR. The feasibility of the proposed UFR monitoring system was evaluated by three experiments in terms of data loss and accuracy.Keywords: URF, embedded linux, GPS, inverse RTK, monitoring system

Estimation of relative ionospheric delay by GNSS Algorithm for Accuracy and Safety

In this paper, a GNSS software package named GAFAS (GNSS Algorithm For Accuracy and Safety) is introduced. GAFAS is a general-purpose software package developed since 2005. It is written in C/C++ language being purposed to build diversified GNSS infrastructure and user applications. Currently, GAFAS is upgrading its capability in medium/long baseline integer ambiguity resolution for more accurate ionospheric delay estimation. To verify the validity of its concept, an experiment was performed with real measurements.