• Proceedings of the Korean Information Science Society Conference
• /
• 2000.04a
• /
• pp.101-103
• /
• 2000

본 논문에서는 GPS(Global Positioning System)와 추측 항법 시스템인 DR(Dead Reckoning)을 혼합 구성한 수신보드의 운영을 위한 Embedded 운영체제를 설계하고 구현 하였다. 이 운영체제는 실시간으로 인공위성으로부터 수신되어지는 Raw Measurement 획득, GPS 항법, 자세 결정, 통합항법, 위성 추적을 수행하는 태스크들을 우선순위 기반으로 처리하는 선점형(Preemptive) 스케쥴링 방식을 채택한 실시간 운영체제 이다. 본 논문에서는 자세 결정용 GPS와 DR 센서를 이용한 통합시스템보드를 위한 실시간 운영체제의 개발 환경, 운영체제의 구조와 개발 내용에 대해 언급하였다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.4B
• /
• pp.300-308
• /
• 2012

Many indoor location tracking technologies have been proposed. Generally indoor location tracking using TOA signal is used, there is a weak point that it's difficult to track the location due to obstacles like a refraction, reflection and dispersion of radio wave. In this paper, we apply ETOA(Estimated-TOA) algorithm in NLOS(Non-Line-Of-Sight) environment to solve above problem. In NLOS environment, TOA value between Beacon and Mobile node is predicted by ETOA algorithm and the tracking of indoor location is also possible to identify using two NLOS beacons of three beacons by this algorithm. We show that the proposed algorithm is accurate location tracking is accomplished using the applying the proposed algorithm to indoor moving robot and the inertia sensor of robot and Kalman filter algorithm.

• KIISE Transactions on Computing Practices
• /
• v.23 no.3
• /
• pp.148-155
• /
• 2017

In this paper, we develop a new system to estimate the step count for a smartphone user. The system analyzes data obtained from the accelerometer, magnetic sensor, and gyroscope of an android smartphone to extract pattern information of human steps. We conduct an experiment and evaluation to confirm that the proposed system successfully estimates the number of steps with 96% accuracy when hand-held and 95.5% accuracy when in-pocket. In addition, we found that detection errors were caused by human motions such as touching the screen, shaking the device up and down, sitting up and sitting down, and waving the phone around.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.8
• /
• pp.744-749
• /
• 2012

In this paper, we suggest that how to improve an accuracy of mobile robot's localization by using the sensor network information which fuses the machine vision camera, encoder and IMU sensor. The heading value of IMU sensor is measured using terrestrial magnetism sensor which is based on magnetic field. However, this sensor is constantly affected by its surrounding environment. So, we isolated template of ceiling using vision camera to increase the sensor's accuracy when we use IMU sensor; we measured the angles by pattern matching algorithm; and to calibrate IMU sensor, we compared the obtained values with IMU sensor values and the offset value. The values that were used to obtain information on the robot's position which were of Encoder, IMU sensor, angle sensor of vision camera are transferred to the Host PC by wireless network. Then, the Host PC estimates the location of robot using all these values. As a result, we were able to get more accurate information on estimated positions than when using IMU sensor calibration solely.

• Journal of Korean Society for Geospatial Information Science
• /
• v.2 no.2 s.4
• /
• pp.81-88
• /
• 1994

The CNS(Car Navigation System) is used more generally in driver aid system than ALV(Auto nomous Land Vehicle) research area. In this paper we developed a new position tracking algorithm for the Global Path Planning in the CNS. In japan, CNS is already well developed and, thesedays they sell CNS products about $400{\sim}500$ thousands per year, and USA and European Communications(EC), too. In Korea, studies of the first generation CNS, which finds current location of a navigating vehicle and displays its location in a Digital-Map with real-time are progressing but still in the beginning step. Therefore a new position tracking algorithm is presented, which reduces vehicle position error dramatically by fusing GPS and dead-reckoning sensors. And the validity of our algorithm is demonstrated by the experimental results with the real car.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.4
• /
• pp.332-340
• /
• 2013

This paper reports a programming toolkit for implementing localization and navigation of a mobile robot both in real world and simulation. Many of the previous function libraries are difficult to use because of their complexity or lack of usability. The proposed toolkit consist of functions for dead reckoning, motion model, measurement model, and operations on directions or heading angles. The dead reckoning and motion model deals with differential drive robot and bicycle type robot driven by front wheel or rear wheel. The functions can be used for navigation in both real environment and simulation. To prove the feasibility of the toolkit, simulation results are shown along with the results in real environment. It is expected the proposed toolkit is used for test of algorithms for mobile robot navigation such as localization, map building, and obstacle avoidance.

• Journal of Advanced Navigation Technology
• /
• v.18 no.2
• /
• pp.134-141
• /
• 2014

Mobile robots use various sensors for navigation such as wheel encoder, vision sensor, sonar, and laser sensors. Dead reckoning is used with wheel encoder, resulting in the accumulation of positioning errors. For that reason wheel encoder can not be used alone. Too much information of vision sensors leads to an increase in the number of features and complexity of perception scheme. Also Sonar sensor is not suitable for positioning because of its poor accuracy. On the other hand, laser sensor provides accurate distance information relatively. In this paper we propose to extract the angular information from the distance information of laser range finder and use the Kalman filter that match the heading and distance of the laser range finder and those of wheel encoder. For laser scanner with one feature point error may increase much when the feature point is variant or jumping to a new feature point. To solve the problem, we propose to use two feature points and show that the positioning error can be reduced much.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.5
• /
• pp.200-209
• /
• 2018

This study proposed a method to select existing facilities as a landmark in order to reset accumulated errors of dead reckoning in a tunnel difficult to receive GNSS signals in automated driving. First, related standards and regulations were reviewed in order to survey 'variety' on shapes and installation locations as a feature of facilities. Second, 'recognition' on facilities was examined using image and Lidar sensors. Last, 'regularity' in terms of installation locations and intervals was surveyed through related references. The results of this study selected a fire fighting box / lamp (50m), an evacuation corridor lamp (300m), a lane control system (500m), a maximum / minimum speed limit sign and a jet fan as a candidate landmark to reset positioning errors. Based on those facilities, it was determined that error correction was possible. The results of this study are expected to be used in repositioning of automated driving vehicles in a tunnel.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.241-244
• /
• 1997

A dead reckoning navigation system is developed for autonomous mobile robot localization. The navigation system was implemented by novel sensor fusion using a Kalman filter. A differential encoder and the gyroscope error models are developed for the filter. An indirect Kalman filter scheme is adopted to reduce the computational burden and to enhance the navigation system reliability. The filter mutually compensates the encoder errors and the gyroscope errors. The experimental results show that the proposed mobile . robot navigation algorithm provides the reliable position and heading angle of the mobile robot without any help of the external positioning systems.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.4 no.2
• /
• pp.69-78
• /
• 2009

Localization is a very important technique for the mobile robot to navigate in outdoor environment. In this paper, the development of the sensor fusion algorithm for controlling mobile robots in outdoor environments is presented. The multi-sensorial dead-reckoning subsystem is established based on the optimal filtering by first fusing a heading angle reading data from a magnetic compass, a rate-gyro, and two encoders mounted on the robot wheels, thereby computing the dead-reckoned location. These data and the position data provided by a global sensing system are fused together by means of an extended Kalman filter. The proposed algorithm is proved by simulation studies of controlling a mobile robot controlled by a backstepping controller and a cascaded controller. Performances of each controller are compared.