• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.637-648
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• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.5
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• pp.522-526
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• 2015

Generally, underwater unmanned vehicle have adopted an inertial navigation system (INS), dead reckoning (DR), acoustic navigation and geophysical navigation techniques as the navigation method because GPS does not work in deep underwater environment. Even if the tactical inertial sensor can provide very detail measurement during long operation time, it is not suitable to use the tactical inertial sensor for small size and low cost UUV because the tactical inertial sensor is expensive and large. One alternative to INS is attitude heading reference system (AHRS) with the micro-machined electro mechanical system (MEMS) inertial sensor because of MEMS inertial sensor's small size and low power requirement. A cost effective and small size attitude heading reference system (AHRS) which incorporates measurements from 3-axis micro-machined electro mechanical system (MEMS) gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for UUV. The AHRS based MEMS overcome many problems that have inhibited the adoption of inertial system for small UUV such as cost, size and power consumption. Several evaluation experiments were carried out for the validation of the developed AHRS's function and these experiments results are presented. Experiments results prove the fact that the developed MEMS AHRS satisfied the required specification.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.544-546
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• 2004

This paper designs and evaluates a complementary filter for fusion of inertial sensor signals. Specifically, the designed filter is applied to inverted pendulum control where the pendulum's angle information is obtained from low-cost tilt and gyroscope sensors instead of an optical encoder. The complementary filter under consideration is a conventional one which consists of low- and high-pass filters. However, to improve the performance of the filter on the gyroscope, we use an integrator in the filter's outer loop. Frequency responses are obtained with both tilt and gyroscope sensors. Based on the frequency response results, we determine appropriate parameter values for the filter. The performance of the designed complementary filter is evaluated by applying the filter to inverted pendulum control. Experiments show that the performance of the designed filter is comparable to that of an optical encoder and low-cost inertial sensors can be used for inverted pendulum control with the heir of sensor fusion.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.584-586
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• 2005

In this paper, low cost inertial sensor and compass were used instead of encoder for localization of mobile robot. Movements by encoder, movements by inertial sensor and movements by complementary filter with inertial sensor and compass were analyzed. Movement by complementary filter was worse than by only inertial sensor because of imperfection of compass. For the complementary filter to show best movements, compass need to be compensated for position error.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.28-28
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• 2000

Low-cost solid-state inertial sensors of three rate Gyroscopes and a triaxial Accelerometer are evaluated in static and dynamic environments. As a interim result, error models of each inertial sensors are generated. Model parameters with respect to temperature are acquired in static environment. These error models are included in an Extended Kalman Filter(EKF) to compensate bias error due to temperature variation. Experimental results in dynamic environment are included to show the validity of the each error model and the performance improvement of a compensated low cost inertial sensors for a navigational application

• International Journal of Aeronautical and Space Sciences
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• v.12 no.4
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• pp.371-378
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• 2011

In this paper, the wavelet denoising techniques using thresholding method are applied to the low cost micro electromechanical system (MEMS)-global positioning system(GPS) integrated system. This was done to improve the navigation performance. The low cost MEMS signals can be distorted with conventional pre-filtering method such as low-pass filtering method. However, wavelet denoising techniques using thresholding method do not distort the rapidly-changing signals. They can reduce the signal noise. This paper verified the improvement of the navigation performance compared to the conventional pre-filtering by simulation and experiment.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.107-111
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• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.525-528
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• 2003

In this paper, we present a novel idea to integrate low cost Inertial Measurement Unit(IMU) and Differential Global Positioning System (DGPS) for Telematics applications. As well known, low cost IMU produces large positioning and attitude errors in very short time due to the poor quality of inertial sensor assembly. To conquer the limitation, we present a bimodal approach for integrating IMU and DGPS, taking advantage of positioning and orientation data calculated from CCD images based on photogrammetry and stereo-vision techniques. The positioning and orientation data from the photogrammetric approach are fed back into the Kalman filter to reduce and compensate IMU errors and improve the performance. Experimental results are presented to show the robustness of the proposed method that can provide accurate position and attitude information for extended period for non-aided GPS information.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.884-887
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• 2023

Recently, research has been actively conducted to overcome the limitations of high-priced single sensors and reduce costs through the convergence of low-cost multi-variable sensors. This paper estimates state variables through asynchronous Kalman filters constructed using CVXPY and uses Cvxpylayers to compare and learn state variables estimated from CVXPY with true value data to estimate filter parameters of low-cost sensors fusion.