Sukchang Yun

On the Mode-Matched Control of MEMS Vibratory Gyroscope via Phase-Domain Analysis and Design

This paper investigates a novel method for the mode-matched control of a microelectromechanical systems (MEMS) vibratory gyroscope through a phase-domain analysis. Compared with the previous works, the proposed method presents a simple and robust automatic mode tuning scheme for sensitivity enhancement. In designing the mode-matched control loop, the resonant characteristics of the driving axis are used as the reference mode. Then, the phase difference between sense and drive modes at the resonant frequency of drive mode is used to generate a control signal for phase error regulation. For the control loop design, a linear phase-locked loop is adapted. Through the simulation using practical MEMS gyroscope parameters, the mode-matching performance and robustness of the designed control loop is demonstrated. It is also shown that coupling effect yields no degradation of output sensitivity. Finally, the experimental results obtained by implementing the electronics of mode-matched control verify the feasibility of the proposed method.

Integrated Navigation Design Using a Gimbaled Vision/LiDAR System with an Approximate Ground Description Model

This paper presents a vision/LiDAR integrated navigation system that provides accurate relative navigation performance on a general ground surface, in GNSS-denied environments. The considered ground surface during flight is approximated as a piecewise continuous model, with flat and slope surface profiles. In its implementation, the presented system consists of a strapdown IMU, and an aided sensor block, consisting of a vision sensor and a LiDAR on a stabilized gimbal platform. Thus, two-dimensional optical flow vectors from the vision sensor, and range information from LiDAR to ground are used to overcome the performance limit of the tactical grade inertial navigation solution without GNSS signal. In filter realization, the INS error model is employed, with measurement vectors containing two-dimensional velocity errors, and one differenced altitude in the navigation frame. In computing the altitude difference, the ground slope angle is estimated in a novel way, through two bisectional LiDAR signals, with a practical assumption representing a general ground profile. Finally, the overall integrated system is implemented, based on the extended Kalman filter framework, and the performance is demonstrated through a simulation study, with an aircraft flight trajectory scenario.

IMU/Vision/Lidar integrated navigation system in GNSS denied environments

This paper aims to develop IMU/Vision/Lidar integrated navigation system which can provide accurate relative navigation information in GNSS denied environments. The developed integrated navigation system consists of an IMU, a vision sensor and a Lidar sensor. In order to overcome limitation of low accuracy of MEMS inertial navigation solution when GNSS signal cannot be used, 2-dimensional optical flow vector from vision sensor and Lidar range information between the system and ground surface are used. Two aided sensor data are complementarily employed in the system integration. Basically, by using the optical flow vector and relative height between vision sensor and ground surface, accuracy of the horizontal position estimates is improved. At the same time, by using compensated Lidar measurement, accurate vertical position can be estimated. The overall integrated navigation filter is constructed based on Extended Kalman Filter approach. Feasibility of the navigation filter is proven via simulation results. Finally, with real hardware system, an outdoor test is carried out for analyzing performance of the proposed integrated navigation system.

Real-time performance test of an vision-based inertial SLAM

Despite its precise positioning performance, a GPS based navigation system may require the reference or augmentation station in close boundaries and is liable to be affected by satellite observation environments. Thus, this paper presents a vision-based inertial SLAM navigation system which can operate in a real-time manner and uses purely unknown feature points in order to cope with the limited GPS/INS integration environment. And real-time performance of the presented system is verified via indoor test.

INS/Vision Sensor Integrated System for Precise Relative Position Estimation using Landmark

Despite precise relative positioning performance of a GPS based navigation system, it may require the reference station in close boundaries and can be affected by satellite observation environments. Thus, this paper presents an INS and vision sensor integrated system, which uses a combination of feature points and the landmark whose position is already known in order to overcome the limitation of GPS-only system. The proposed system has the advantage of implementing three-dimensional navigation using the geometry of landmark images. The simulation verifies the performance of integrated navigation system, by which the performance enhancement in estimating relative position of the vehicle is proved effectively.

An Efficient Attitude Reference System Design Using Velocity Differential Vectors under Weak Acceleration Dynamics

This paper proposes a new method achieving computationally efficient attitude reference system for low cost strapdown sensors and microprocessor platform. The main idea in this method is to define and compare velocity differential vectors, geometrically computed from INS and GPS data with different update rate, for generating attitude error measurements which is further used for filter construction. A quaternion based Kalman filter configuration is applied for the attitude estimation with the adapted measurement model of differential vector comparison. Linearized model for Extended Kalman Filter and low pass filtered characteristics of measurement greatly extend the affordability of the proposed algorithm to the field of simple low cost embedded systems. For performance verification, experiment are done employing a practical low cost MEMS IMU and GPS receiver specification. Performance comparison with a high grade navigation system demonstrated good estimation result.

Implementation and Flight Test Performance Analysis of vSLAM Aided Integrated Navigation System for Rotary UAV

In this paper, vSLAM aided integrated navigation system is implemented and performance analysis of the system is completed via flight test. The system can suppress divergence of position error of INS only system by updating vSLAM correction information when temporary GPS signal outage occurs in bad radio condition. In the flight test, integrated hardware containing GPS, IMU and camera is loaded under RC electric helicopter. Performance of the integrated navigation system is verified by comparing estimated position of INS/vSLAM system with that of INS only system.

A study on the automatic gain control loop design for the resonant accelerometer

In this paper, we introduce a new design approach for self-sustained resonant acceleration, that uses an automatic gain control (AGC) to achieve a stabilized oscillation dynamics. Fundamental idea of this acceleration is to maintain uniform amplitude of oscillation. Through system modeling and loop transformation considering the envelope of oscillation, the controller is design to keep up uniform amplitude of oscillation under dynamic input acceleration. The simulation shows the feasibility of the proposed accelerometer design, which is applicable to control grade inertial sensing system in industrial and civil application fields.

Augmented Feature Point Initialization Method for Vision/Lidar Aided 6-DoF Bearing-Only Inertial SLAM

This study proposes a novel feature point initialization method in order to improve the accuracy of feature point positions by fusing a vision sensor and a lidar. The initialization is a process that determines three dimensional positions of feature points through two dimensional image data, which has a direct influence on performance of a 6-DoF bearing-only SLAM. Prior to the initialization, an extrinsic calibration method which estimates rotational and translational relationships between a vision sensor and lidar using multiple calibration tools was employed, then the feature point initialization method based on the estimated extrinsic calibration parameters was presented. In this process, in order to improve performance of the accuracy of the initialized feature points, an iterative automatic scaling parameter tuning technique was presented. The validity of the proposed feature point initialization method was verified in a 6-DoF bearing-only SLAM framework through an indoor and outdoor tests that compare estimation performance with the previous initialization method.

Implementation of Virtual Instrumentation based Realtime Vision Guided Autopilot System and Onboard Flight Test using Rotory UAV

This paper investigates the implementation and flight test of realtime vision guided autopilot system based on virtual instrumentation platform. A graphical design process via virtual instrumentation platform is fully used for the image processing, communication between systems, vehicle dynamics control, and vision coupled guidance algorithms. A significatnt ojective of the algorithm is to achieve an environment robust autopilot despite wind and an irregular image acquisition condition. For a robust vision guided path tracking and hovering performance, the flight path guidance logic is combined in a multi conditional basis with the position estimation algorithm coupled with the vehicle attitude dynamics. An onboard flight test equipped with the developed realtime vision guided autopilot system is done using the rotary UAV system with full attitude control capability. Outdoor flight test demonstrated that the designed vision guided autopilot system succeeded in UAVs hovering on top of ground target within about several meters under geenral windy environment.