• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.24-30
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• 2015

As the complexity of urban growth out of people with disabilities are subject to many restrictions. In particular, visually impaired people are not allowed to perceive objects as well as objects around the environment, it suffers from a number of outdoor activities to environmental challenges. But the outdoors environment of the current domestic reality is lacking a lot of facilities to assist the activities of people with disabilities, including the visually impaired. In this paper, we propose a navigation system to help the visually impaired in outdoor activities. The proposed system was applied to test the Jeungpyeong campus of Korea National University of Transportation for the exam.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.82-90
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• 2011

This paper proposes an advanced outdoor localization algorithm of a GPS(global positioning system)-INS(inertial navigation system) integration system. In order to reduce noise from the internal INS sensors, discrete wavelet transform and variable threshold method are utilized. The UPF (unscented particle filter) combines GPS information and INS signals to implement precise outdoor localization algorithm and to reduce noise caused by the acceleration, deceleration, and unexpected slips. The conventional de-noising method is mainly carried out using a low pass filter and a high pass filter which essentially result in signal distortions. This newly proposed system utilizes the vibration information of actuator according to fluctuations of the velocity to minimize signal distortions. The UPF also resolves non-linearities of the actuator and non-normal distributions of noises. Effectiveness of the proposed algorithm has been verified through the real experiments and the results are demonstrated.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.651-662
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• 2010

This paper describes a method to identify objects for autonomous navigation of an outdoor mobile robot. To identify objects, the robot recognizes the object from an image taken by moving robot on outdoor environment. As a beginning, this paper presents the candidates for a segment of region to building of artificial object, sky and trees of natural objects. Then we define their characteristics individually. In the process, we segment the regions of the objects included by preprocessing using multiple features. Multiple features are HSI, line segments, context information, hue co-occurrence matrix, principal components and vanishing point. An analysis of building identifies the geometrical properties of building facet such as wall region, windows and entrance. The building as intersection in vertical and horizontal line segment of vanishing point extracts the mesh. The wall region of building detect by merging the mesh of the neighbor parallelograms that have similar colors. The property estimates the number of story and rooms in the same floors by merging skewed parallelograms of the same color. We accomplish the result of image segmentation using multiple features and the geometrical properties analysis of object through experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.381-389
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• 2012

This paper proposes a method which corrects location data of GPS for navigation of outdoor mobile robot. The method uses a Bayesian filter approach called the particle filter(PF). The method iterates two procedures: prediction and correction. The prediction procedure calculates robot location based on translational and rotational velocity data given by the robot command. It incorporates uncertainty into the predicted robot location by adding uncertainty to translational and rotational velocity command. Using the sensor characteristics of the GPS, the belief that a particle assumes true location of the robot is calculated. The resampling from the particles based on the belief constitutes the correction procedure. Since usual GPS data includes abrupt and random noise, the robot motion command based on the GPS data suffers from sudden and unexpected change, resulting in jerky robot motion. The PF reduces corruption on the GPS data and prevents unexpected location error. The proposed method is used for navigation of a mobile robot in the 2011 Robot Outdoor Navigation Competition, which was held at Gwangju on the 16-th August 2011. The method restricted the robot location error below 0.5m along the navigation of 300m length.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1778-1779
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• 2011

The location of outdoor mobile robot is obtained using global coordinates from GPS. However, the error generated by GPS is about 10m ~ 100m, so the precise control is difficult. D-GPS has the error value of 1m and it is very accurate, but the price is very expensive. In this paper, a method to reduce the error in global coordinates is proposed using two low-cost GPS for the autonomous navigation control of outdoor mobile robot.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.69-76
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• 2017

Bluetooth Low Energy (BLE), also known as Bluetooth Smart, has ultra-low power consumption; in fact, BLE-enabled devices can run on a single coin cell battery for several years. In addition, BLE can estimate the approximate distance between two devices using the Received Signal Strength Indication (RSSI) feature, enabling relatively precise navigation in indoor and small outdoor areas where GPS is not an option. In this paper, an experimental setup is presented in which BLE is used for navigation within a small outdoor area. BLE-based beacons are installed in fixed positions, which periodically transmit a universally unique identifier (UUID). A smart device receives the UUID and sends it to a database server using cellular or Wi-Fi technology. The server returns fixed position information corresponding to the received UUID codes, and the smart device uses that information to compute its current position based on relative signal strengths, and display it on a map. These results demonstrate the successful application of BLE technology for navigation in small outdoor areas. This system can be implemented for indoor navigation as well.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.129-135
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• 2013

In this paper, a high precision outdoor positioning system is newly proposed using multiple GPS receivers based on the Extended Kalman Filter (EKF). Typically, the GPS signal has the instantaneous errors that degrade the positioning seriously. Using the multiple GPS receivers instead of an expensive DGPS receiver, the positioning reliability and accuracy are improved in this research as a low cost solution. To incorporate the small displacement, an INS data have been tightly coupled to the GPS data, which has the inherit disadvantage of the cumulative error occurring over time. To achieve a stabilized and accurate positioning system, the multiple GPS receiver data are fused with the INS data through the EKF process. Through real navigation experiments of an outdoor mobile robot, the performance of the proposed system has been verified to be accurate comparable to DGPS system with a lower cost.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.5
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• pp.483-489
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• 2015

In this paper, we propose monocular visual simultaneous localization and mapping (SLAM) in the large-scale environment. The proposed method continuously computes the current 6-DoF camera pose and 3D landmarks position from video input. The proposed method successfully builds consistent maps from challenging outdoor sequences using a monocular camera as the only sensor. By using a binary descriptor and metric-topological mapping, the system demonstrates real-time performance on a large-scale outdoor dataset without utilizing GPUs or reducing input image size. The effectiveness of the proposed method is demonstrated on various challenging video sequences.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.429-435
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• 2011

This paper presents a sensor fusion-based estimation of heading and a Bezier curve-based motion planning for unmanned ground vehicle. For the vehicle to drive itself autonomously and safely, it should estimate its pose with sufficient accuracy in reasonable processing time. The vehicle should also have a path planning algorithm that enables to adapt to various situations on the road, especially at intersections. First, we address a sensor fusion-based estimation of the heading of the vehicle. Based on extended Kalman filter, the algorithm estimates the heading using the GPS, IMU, and wheel encoders considering the reliability of each sensor measurement. Then, we propose a Bezier curve-based path planner that creates several number of path candidates which are described as Bezier curves with adaptive control points, and selects the best path among them that has the maximum probability of passing through waypoints or arriving at target points. Experiments under various outdoor conditions including at intersections, verify the reliability of our algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.67-70
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• 2006

For a long time the GSM and GPS world has been living side by side. There have been some half hearted attempts to invoke the GPS unit into GSM units but performance and usability has suffered. The A-GPS approach is interesting and we wanted to verify general performance for fleet application and alarm/emergency situations. The result is now promising but still more to enhancements are anticipated. We achieved near acceptable availability (${\sim}70%$) under indoor conditions. Accuracy was as anticipated (50-100 meter) as we are using reflected signals. Time to first fix is in general good in outdoor conditions but too long for indoor conditions (45-60 seconds). We would like manufactures to put some more effort to get better performance in the future as test with Nordnav high sensitivity measurement system proves that conditions indoor are at a level where better performance should be possible.