• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1822-1829
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• 2008

Calibration of an MEMS inertial measurement unit is very important process for obtaining precise navigation performance. In this paper, one method is proposed to overcome a limitations on cost and efficiency using a relatively higher grade sensor and a rate table. The same dynamic input is applied to both the reference and the target sensors during and after calibration process, then the results are analyzed. The experimental results show that the proposed method is very effective and useful in practice.

• Journal of Advanced Navigation Technology
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• v.8 no.2
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• pp.105-111
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• 2004

This paper describes the design of navigation system for an unmanned target drone which is operated by Korean army as for anti-air gun shooting training. Current target drone is operated by pilot control of on-board servo motor via remote control system. Automatic flight control system for the target drone greatly reduces work load of ground pilot and can increase application area of the drone. Most UAVs being operated nowdays use high-priced sensors as AHRS and IMU to measure the attitude, but those are costly. This paper introduces the development of low-cost automatic flight control system with low-cost sensors. The integrated automatic flight control system has been developed by integrating combining power module, switching module, monitoring module and RC receiver as an one module. The performance of navigation for low cost unmanned aerial vehicle, unmanned target drone as our test bed in this paper is verified by both Hardware in the loop simulation(HILS) to test performance of GPS as GPS output frequency high and results of flight test.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.237-242
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• 2023

Research on high-performance walking robots is being actively conducted, and quadruped walking robots are receiving a lot of attention due to their excellent mobility and adaptability on uneven terrain, but they are difficult to introduce and utilize due to high cost. In this paper, to increase utilization by applying intelligent functions to a low-cost quadruped robot, we present a method of improving uneven terrain overcoming ability by mounting IMU and reinforcement learning on embedded board and automatically detecting objects using camera and deep learning. The robot consists of the legs of a quadruped mammal, and each leg has three degrees of freedom. We train complex terrain in simulation environments with designed 3D model and apply it to real robot. Through the application of this research method, it was confirmed that there was no significant difference in walking ability between flat and non-flat terrain, and the behavior of performing person detection in real time under limited experimental conditions was confirmed.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.47-53
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• 2006

In this contribution, an integration of seamless navigation system for the pedestrian is introduced. To overcome the GPS outages in various situations, multi-sensor of GPS, INS, electronic barometer and compass are considered in one Extented Kalman filter. Especially, the integrated system is designed for low-cost for the practical applications. Therefore, a MEMS IMU is considered, and the low quality of the heading is compensated by the electronic compass. In addition, only the pseudoranges from GPS measurements are considered for possible real-time application so that the degraded height is also controlled by a barometer. The mathematical models for each sensor with systematic errors such as biases, scale factors are described in detail and the results are presented in terms of a covariance analysis as well as the position and attitude errors compared to the high-grade GPS/INS combined solutions. The real application scenario of GPS outage is also investigated to assess the feasible accuracy with respect to the outage period. The description on the current status of the development and future research directions are also stated.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.797-807
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• 2014

The Sea Surface Temperature (SST) is one of the most important oceanic environmental factors in determining the change of marine environments and ecological activities. Satellite thermal infrared images can be effective for understanding the global trend of sea surface temperature due to large scale. However, their low spatial resolution caused some limitations in some areas where complicated and refined coastal shapes due to many islands are present as in the Korean Peninsula. The coastal ocean is also very important because human activities interact with the environmental change of coastal area and most aqua farming is distributed in the coastal ocean. Thus, low-cost airborne thermal infrared remote sensing with high resolution capability is considered for verifying its possibility to extract SST and to monitor the changes of coastal environment. In this study, an airborne thermal infrared system was implemented using a low-cost and ground-based thermal infrared camera (FLIR), and more than 8 airborne acquisitions were carried out in the western coast of the Korean Peninsula during the periods between May 23, 2012 and December 7, 2013. The acquired thermal infrared images were radiometrically calibrated using an atmospheric radiative transfer model with a support from a temperature-humidity sensor, and geometrically calibrated using GPS and IMU sensors. In particular, the airborne sea surface temperature acquired in June 25, 2013 was compared and verified with satellite SST as well as ship-borne thermal infrared and in-situ SST data. As a result, the airborne thermal infrared sensor extracted SST with an accuracy of $1^{\circ}C$.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.291-298
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• 2022

Recently developed drones are inexpensive and very convenient to operate. As a result, the production and utilization of spatial information using drones are increasing. However, most drones acquire images with a low-cost global navigation satellite system (GNSS) and an inertial measurement unit (IMU). Accordingly, the accuracy of the initial location and rotation angle elements of the image is low. In addition, because these drones are small and light, they can be greatly affected by wind, making it difficult to maintain a certain overlap, which degrades the positioning accuracy. Therefore, in this study, images are taken at different times in order to analyze the positioning accuracy according to changes in certain exterior orientation parameters. To do this, image processing was performed with Pix4D Mapper and the accuracy of the results was analyzed. In order to analyze the variation of the accuracy according to the exterior orientation parameters in detail, the exterior orientation parameters of the first processing result were used as meta-data for the second processing. Subsequently, the amount of change in the exterior orientation parameters was analyzed by in a strip-by-strip manner. As a result, it was proved that the changes of the Omega and Phi values among the rotation elements were related to a decrease in the height accuracy, while changes in Kappa were linked to the horizontal accuracy.

• Spatial Information Research
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• v.21 no.6
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• pp.57-67
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• 2013

GPS based car navigation systems show significant problems in such environment as a tunnel, a road surrounded by high buildings. In this study, we thus propose a method to determine positions and attitudes using only in-vehicle sensory data without a GPS. To check the feasibility of this method, we constructed a system to acquire in-vehicle sensory data and reference data simultaneously. We acquired test data using this system, estimated the trajectory based on the proposed method and evaluated the accuracy of both the sensory data and the trajectory. The speed and angular velocities provided by the in-vehicle sensors include 1.1 km/h and 0.8 deg/s RMS errors, respectively. The estimated trajectory using these data shows 20.8 m RMS errors for a 15 minute drive. In future, if we further combine additional sensors such as a camera and a GPS, we can achieve a high accurate navigation system at a low cost without an expensive high-grade external IMU.

• Current Research on Agriculture and Life Sciences
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• v.32 no.3
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• pp.159-163
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• 2014

Agricultural unmanned helicopters have become a new paradigm for aerial application. Yet, such agricultural helicopters require easy and affordable attitude control systems. Therefore, this study presents an affordable attitude measurement system using a DCM (direction cosine matrix) algorithm that would be applied to agricultural unmanned helicopters. An IMU using a low-cost MEMS and an algorithm to estimate the attitude of the helicopter were applied in a gimbals structure to evaluate the accuracy of the attitude measurements. The estimation errors in the attitude were determined in comparison with the true angles determined by absolute position encoders. The DCM algorithm and sensors showed an accuracy of about 1.1% for the roll and pitch angle estimation. However, the accuracy of the yaw angle estimation at 3.7% was relatively larger. Such errors may be due to the magnetic field of the stepping motor and encoder system. Notwithstanding, since the intrinsic behavior of the agricultural helicopter remains steady, the determination of attitude would be reliable and practical.