• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.12
• /
• pp.1697-1703
• /
• 2014

In this paper, we propose an indoor RSSI (received signal strength indication)-based localization method that uses virtually overlapped visible light with an indoor LED lighting system. In our system, a photodiode (PD) measures the RSSI from LED lamps that blink in one row or column units. Subsequently, the RSSI is used to obtain the horizontal distances between the LED lamps and the receiver with the predetermined characteristics curve, R-D curve, that represents the relation between the RSSI and the horizontal distances. When the controlled LED lamps blink in one row or column units, the R-D curve at the border of the LED lamps is different because of the weak lighting, which results in the position sensing error of the receiver. The deviation of the optical power of each LED also causes the error. To solve these problems, we propose a method that overlaps the visible light through the numerical operation at the receiver side without any modification of the light source side. Our proposed method has been simulated in a room measuring $1.2{\times}1.2{\times}1.8m^3$ considering the effect of the error on the optical power of the LED. The simulation result shows that the proposed method eliminates the error condition with the R-D curve and achieves an average positioning error of 13.4 mm under the error rate 3% of the optical power.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.4
• /
• pp.507-512
• /
• 2015

In the wireless localization systems using range measurements, iteration method-based approximated solutions have been used. Also, linear closed-form solutions have been investigated in the light of local minimum problem and computational load. However, each closed-form solution has individual error factors that cause usage limit of the solutions. In this paper, a fusion method integrating two representative closed-form solutions is presented. The presented method cancels the error factors of each solution out. Weights for integrating the standalone solutions are determined using the error factors-based fuzzy method. The performance of the proposed method is verified using some simulation results.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.2
• /
• pp.98-109
• /
• 2010

This paper deals with a new method of unmanned aerial vehicle (UAV) recovery when a UAV fails to get a global positioning system (GPS) signal at an unprepared site. The proposed method is based on the simultaneous localization and mapping (SLAM) algorithm. It is a process by which a vehicle can build a map of an unknown environment and simultaneously use this map to determine its position. Extensive research on SLAM algorithms proves that the error in the map reaches a lower limit, which is a function of the error that existed when the first observation was made. For this reason, the proposed method can help an inertial navigation system to prevent its error of divergence with regard to the vehicle position. In other words, it is possible that a UAV can navigate with reasonable positional accuracy in an unknown environment without the aid of GPS. This is the main idea of the present paper. Especially, this paper focuses on path planning that maximizes the discussed ability of a SLAM algorithm. In this work, a SLAM algorithm based on extended Kalman filter is used. For simplicity's sake, a blimp-type of UAV model is discussed and three-dimensional pointed-shape landmarks are considered. Finally, the proposed method is evaluated by a number of simulations.

• The Journal of Korea Robotics Society
• /
• v.6 no.1
• /
• pp.58-68
• /
• 2011

This paper proposes a technique of indoor localization for mobile robot by so called indoor GPS and EKF. Basically the concept of indoor GPS is similar outdoor GPS, and the indoor GPS gets distances between Anchors and Tag by a ranging method of CSS and then estimates the coordinate by distances and known Anchor positions. After we performed performance test of indoor GPS system in ideal and multipath environment, we configured that the indoor GPS has internal error factors and external error factors. This paper handled a multipath problem belonging to external error factors. At first various direct physical method are introduced to fix the multipath problems, and as expected we got errors corrected considerably. And then the method of selective anchors for indoor GPS is applied. With these two level improvement of indoor GPS performance, EKF(Extended Kalman Filter) is applied to mobile robot in indoor environment. The usefulness of the proposed methods are shown by a series of experiments in a environment giving contaminated data by multipath.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.12
• /
• pp.1061-1067
• /
• 2016

Position of the vehicle for driving is essential to autonomous navigation. However, there appears GPS position error due to multipath which is occurred by tall buildings in downtown area. In this paper, GPS position error is corrected by using camera sensor and highly accurate map made with 3D-Lidar. Input image through inverse perspective mapping is converted into top-view image, and it works out map matching with the map which has intensity of 3D-Lidar. Performance comparison was conducted between this method and traditional way which does map matching with input image after conversion of map to pinhole camera image. As a result, longitudinal error declined 49% and complexity declined 90%.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.5
• /
• pp.1627-1632
• /
• 2010

The performance characteristics and the disadvantages in the compensation algorithm based on the Measured Error Patterns of Distance that is already developed are analyzed, and the localization compensation algorithm(DCA2) based on measured error patterns of distance in WPAN that is the enhanced version of DCA1 is supposed in this paper. From the experimental results, it is confirmed that the localization performance of DCA1 and DCA2 is superior than SDS-TWR as each average above 60% and 75% of the total localizing measurement points in 2 experimental regions, and the localization performance of DCA2 is especially better than SDS-TWR as 91% of the points in $15m{\times}15m$ experimental region. In addition to this, it is confirmed that DCA2 is better than DCA1 as each 16% and 22% of the total localizing measurement points in $10m{\times}10m$ and $15m{\times}15m$ scaled experimental regions, and the average localization errors of DCA1 and DCA2 are lower than SDS-TWR to each 7~12% and 20%. Thus, it can be inferred that DCA2 is the best localization algorithm among 3 localization algorithms SDS-TWR, and DCA2.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.536-538
• /
• 2006

The most important factor of mobile robot is to build a map for surrounding environment and estimate its localization. This paper proposes a real-time localization and map building method through 3-D reconstruction using scale invariant feature from monocular camera. Mobile robot attached monocular camera looking wall extracts scale invariant features in each image using SIFT(Scale Invariant Feature Transform) as it follows wall. Matching is carried out by the extracted features and matching feature map that is transformed into absolute coordinates using 3-D reconstruction of point and geometrical analysis of surrounding environment build, and store it map database. After finished feature map building, the robot finds some points matched with previous feature map and find its pose by affine parameter in real time. Position error of the proposed method was maximum. 8cm and angle error was within $10^{\circ}$.

• Journal of Positioning, Navigation, and Timing
• /
• v.9 no.3
• /
• pp.183-189
• /
• 2020

In emergency rescue situations, the localization accuracy of the rescue requestor is a very important factor in determining the success or failure of the rescue. Indoors where Global Navigation Satellite System (GNSS) is not operated, there is no choice but to use Wi-Fi or LTE signals. However, the performance of the current emergency rescue system utilizing those RF signals is exceedingly low. In this study, the effectiveness of the surface correlation technology using the accumulated signal pattern of RF signals was verified in relation to the emergency localization technology. To validate the proposed system, we configured and tested an emergency rescue scenario in multi-floors building. When the emergency rescue was requested, it was confirmed that the initial localization error was large owing to the short length of the accumulated signal pattern. However, the localization error decreased over time, which eventually led to the accurate location information being delivered to the rescuer.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.4
• /
• pp.372-378
• /
• 2013

In this paper, we research the localization problem of the crawler-type inspection robot for underwater structure which travels an outer wall of underwater structure. Since various factors of the underwater environment affect an encoder odometer, it is hard to localize robot itself using only on-board sensors. So in this research we used a depth sensor and an IMU to compensate odometer which has extreme error in the underwater environment through using Extended Kalman Filter(EKF) which is normally used in mobile robotics. To acquire valid measurements, we implemented precision sensor modeling after assuming specific situation that robot travels underwater structure. The depth sensor acquires a vertical position of robot and compensates one of the robot pose, and IMU is used to compensate a bearing. But horizontal position of robot can't be compensated by using only on-board sensors. So we proposed a localization algorithm which makes horizontal direction error bounded by using kinematics relationship. Also we implemented computer simulations and experiments in underwater environment to verify the algorithm performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.1
• /
• pp.41-46
• /
• 2006

Ubiquitous location based services, offer helpful services anytime and anywhere by using real-time location information of objects based on ubiquitous network. Particularly, autonomous mobile robots can be a solution for various applications related to ubiquitous location based services, e.g. in hospitals, for cleaning, at airports or railway stations. However, a meaningful and still unsolved problem for most applications is to develop a robust and cheap positioning system. A typical example of position measurements is dead reckoning that is well known for providing a good short-term accuracy, being inexpensive and allowing very high sampling rates. However, the measurement always has some accumulated errors because the fundamental idea of dead reckoning is the integration of incremental motion information over time. The other hand, a localization system using RFID offers absolute position of robots regardless of elapsed time. We construct an absolute positioning system based on RFID and investigate how localization technique can be enhanced by RFID through experiment to measure the location of a mobile robot. Tags are placed on the floor at 5cm intervals in the shape of square in an arbitrary space and the accuracy of position measurement is investigated . To reduce the error and the variation of error, a weighting function based on Gaussian function is used. Different weighting values are applied to position data of tags since weighting values follow Gaussian function.