• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.22-30
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• 2009

GPS and INS integrated systems are expected to become commonly available as a result of low cost Micro-Electro-Mechanical Sensor (MEMS) technology. However, the current performance achieved by low cost sensors is still relatively poor due to the large inertial sensor errors. This is particularly prevalent in the urban environment where there are significant periods of restricted sky view. To reduce the inertial sensor error, GPS and low cost INS are integrated using a Loosely Coupled Kalman Filter architecture which is appropriate in most applications where there is good satellite availability. In this paper, we present the GPS/INS sensor Integration using Loosely Coupled Kalman Filter approach. We also compare the simulation results of Wander Azimuth Strapdown Mechanization Scheme with the reference values generated by the ZH35C trajectory simulator that is describe mathematically either by the geometry of the path, or as the position of the object over time.

• The Journal of Korea Robotics Society
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• v.4 no.2
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• pp.112-120
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• 2009

In this paper, a localization error recoverymethod based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2433-2435
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• 2000

Inertial Navigation System(INS) provides short-term accurate navigation solution but its error grows with time due to integration characteristics. Meanwhile, Global Positioning System(GPS) provides long-term stable solution but it has poor error characteristics in high dynamic region. So for its synergistic relationship, an integrated INS/GPS systems has been widely used as an advanced navigation system. Generally, two kinds of integration method are used. One is loosely coupled mode which uses GPS-derived position and velocity as measurements in an integrated Kalman filter. The other is tightly coupled one which uses pseudorange and pseudorange rate as Kalman filter measurements. In this paper the system error models and observation models for two kinds of integrated systems are derived, respectively, and their performance are compared through Monte-Carlo simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.937-943
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• 2016

Vertical velocity is critical in many areas, such as the control of unmanned aerial vehicles, fall detection, and virtual reality. Conventionally, the integration of GPS (Global Positioning System) with an IMU (Inertial Measurement Unit) was popular for the estimation of vertical components. However, GPS cannot work well indoors and, more importantly, has low accuracy in the vertical direction. In order to overcome these issues, IMU-barometer integration has been suggested instead of IMU-GPS integration. This paper proposes a new complementary filter for the estimation of vertical velocity based on IMU-barometer integration. The proposed complementary filter is designed to minimize drift error in the estimated velocity by adding PID control in addition to a zero velocity update technique.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.1 s.24
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• pp.5-12
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• 2006

This study describes an SDINS/GPS/ZUPT integration algorithm for land navigation systems. The SDINS error can be decoupled in two parts. The first part is the the Schuler component which does not depend on object motion parameters, and the other is the Non-Schuler part which depends on the product of object acceleration and azimuth error. Azimuth error causes SDINS error in proportion to the traversed distance. The proposed system consists of a GPS/SDINS integration system and an SDINS/ZUPT integration system, which are both realized by an indirect feedforward Kalman filter. The main difference between the two is whether the estimate includes the Non-Schuler error or not, which is decided by the measurement type. Consequently, subtracting GPS/SDINS outputs from SDINS/ZUPT outputs provide the Non-Schuler error information which can be applied to improving azimuth accuracy. Simulation results using the raw data obtained from a van test attest that the proposed SDINS/GPS/ZUPT system is capable of providing azimuth improvement.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.267-275
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• 2013

EKF has been widely used for GPS/INS integration as standard method but EKF has one well-known drawback. if the errors are not within the bounded region, the filter may be divergent. The particle filter has the advantage of the nonlinear and non-gaussian system. This paper proposes a method for compensating the GPS position errors based on the particle filter and presents loosely-coupled GPS/INS integration using proposed algorithm. We used GPS position pattern with particle filter and added attitude kalman filter for improving attitude accuracy. To verify the performance, the proposed method is compared with high cost GPS as reference. In the experimental result, we verified that the accuracy and robust were well improved by the proposed method filter effectively and robustness than by original loosely-coupled integration when vehicle turns at corner.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.3
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• pp.269-275
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• 2001

In land navigation applications, two kinds of GPS/DR integration schemes are commonly used; the loosely-coupled integration scheme and the tightly-coupled one. The loosely-coupled integration filter has a simple structure and is easy to implement. When the number of visible satellites is insufficient, however, it cannot calibrate the errors of the DR sensors. On the contrary the tigthly-coupled integration filter can sup-press the growth of the error in the DR output even when the visibility is poor. However, it has larger com-putation load due to the state dimension and is inconsistent because of the variation in the measurement dimension. This paper presents a GPS/DR integration scheme with dual integration mode. During when the number of visible satellites is sufficient, the proposed scheme operates in a loosely-coupled integration mode. When the visibility becomes poor, it is switched into a tightly-coupled integration mode. Consequently, the pro-posed scheme can calibrate the DR sensors even when the visibility is poor. In addition, its computation time remains constant even if the number of visible satellites increases. Field experiment results show that the performance of the proposed integration method is almost similar to that of the tightly-coupled one.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.798-802
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper, the robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is to use different models (CAD model etc.) known a priori. Also fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Because voting is a very simple or no model is needed for fusion, voting-based fusion of cues is applied. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters, namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.259-264
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• 2006

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. The conventional way of determining Q and R requires good a priori knowledge of the process noises and measurement errors, which normally comes from intensive empirical analysis. Many adaptive methods have been developed to overcome the conventional Kalman filter's limitations. Starting from covariance matching principles, an innovative adaptive process noise scaling algorithm has been proposed in this paper. Without artificial or empirical parameters to be set, the proposed adaptive mechanism drives the filter autonomously to the optimal mode. The proposed algorithm has been tested using road test data, showing significant improvements to filtering performance.