• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.839-846
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• 2022

Traditionally, the construction industry has shown low labor productivity and productivity growth. To improve labor productivity, it must first be accurately measured. The existing method uses work-sampling techniques through observation of workers' activities at certain time intervals on site. However, a disadvantage of this method is that the results may differ depending on the observer's judgment and may be inaccurate in the case of a large number of missed scenarios. Therefore, this study proposes a model to automate labor productivity measurement by monitoring workers' actions using a deep learning-based pose estimation method. The results are expected to contribute to productivity improvement on construction sites.

• 로봇학회논문지
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• 제18권4호
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.825-830
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• 2004

Kinematic calibration of Gough-Stewart platform using a new measurement device is presented in this paper. The device simultaneously measures components of position and orientation using commercially available gadgets. Additional kinematic parameters are defined to model the sources of inaccuracies for the proposed measurement device. Computer simulations reveal that all kinematic parameters of the Gough-Stewart platform and the additional kinematic parameters of the measurement device can be identified with the partial pose measurements of the device. Results also show that identification is robust for the initial errors and the noise in measurements. The device also facilitates the automation of easurement procedure.

• Smart Structures and Systems
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• 제24권5호
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• pp.587-595
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• 2019

Deformation measurement of large scale structures, such as the ground beds of high-rise buildings, tunnels, bridge, and railways, are important for insuring service quality and safety. The pose-relay videometrics method and displacement-relay videometrics method have already presented to measure the pose of non-intervisible objects and vertical subsidence of unstable areas, respectively. Both methods combine the cameras and cooperative markers to form the camera series networks. Based on these two networks, we propose two novel videometrics methods with closed-loop camera series network for deformation measurement of large scale structures. The closed-loop camera series network offers "closed-loop constraints" for the camera series network: the deformation of the reference points observed by different measurement stations is identical. The closed-loop constraints improve the measurement accuracy using camera series network. Furthermore, multiple closed-loops and the flexible combination of camera series network are introduced to facilitate more complex deformation measurement tasks. Simulated results show that the closed-loop constraints can enhance the measurement accuracy of camera series network effectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.1-11
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• 2013

The determination of the pose of the imaging camera is a fundamental problem in computer vision. In the monocular case, difficulties in determining the scene scale and the limitation to bearing-only measurements increase the difficulty in estimating camera pose accurately. Many mobile phones now contain inertial measurement devices, which may lend some aid to the task of determining camera pose. In this study, by means of simulation and real-world experimentation, we explore an approach to monocular camera localization that incorporates both observations of the environment and measurements from accelerometers and gyroscopes. The unscented Kalman filter was implemented for this task. Our main contribution is a novel approach to landmark initialization in a Kalman filter; we characterize the tolerance to noise that this approach allows.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.861-867
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• 2007

A spreader pose control system using dual-electric compasses has been implemented by measuring the skew angle of the spreader with dual-electric compasses. In the conventional spreader pose measurement, CCD cameras, laser sensors or tilt sensors are mostly used. However those sensors are not only sensitive to the weather and disturbances but also expensive to build the system. To overcome the shortcomings, an inexpensive and efficient system to control the spreader pose has been implemented using the dual-magnetic compasses. Since the spreader iron-structures are noise sources to the magnetic compass, it is not considered to use the magnetic compass to measure the orientation of the spreader. An algorithm to eliminate the interferences of metal structures to the dual compasses has been developed in this paper. The 10:1 reduction model of a spreader control system is implemented and the control performance is demonstrated to show the effectiveness of the dual-magnetic compasses proposed in this research.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.366-368
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• 2007

A spreader pose control system using dual-electric compasses has been implemented by measuring the skew angle of the spreader with dual-electric compasses. In the conventional spreader pose measurement, CCD cameras, laser sensors or tilt sensors are mostly used. However those sensors are not only sensitive to the weather and disturbances but also expensive to build the system. To overcome the shortcomings, an inexpensive and efficient system to control the spreader pose has been implemented using the dual-magnetic compasses. Since the spreader iron-structures are noise sources to the magnetic compass, it is not considered to use the magnetic compass to measure the orientation of the spreader. An algorithm to eliminate the interferences of metal structures to the dual compasses has been developed in this paper. The 10:1 reduction model of a spreader control system is implemented and the control performance is demonstrated to show the effectiveness of the dual-magnetic compasses proposed in this research.

• 한국정밀공학회지
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• 제20권2호
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• pp.184-191
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• 2003

A parallel manipulator has high stiffness and all the joint errors on the device are not accumulated at the end -effector unlike a serial manipulator. These are the reasons why the parallel manipulator has been widely used in many fields of industry. In the parallel manipulator, it is very important to predict the exact pose of the end-effector when we want to control the end-effector motion. Installation errors have to be determined in order to predict and control the actual position and pose of the end-effector. This paper presents an algorithm to find the whole 36 joint error components with joint clearance errors and measurement errors considered, when a link length measurement sensor is used and data more than 36 times are acquired for 36 different configurations. A simulation test using this algorithm is performed with a Matlab program which uses the Levenberg-Marquardt method that is known to be efficient for non-linear optimization.

• 로봇학회논문지
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• 제12권4호
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• pp.385-394
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• 2017

Side scanning sonar (SSS) provides valuable information for robot navigation. However using the side scanning sonar images in the navigation was not fully studied. In this paper, we use range data, and side scanning sonar images from UnderWater Simulator (UWSim) and propose measurement models in a feature based simultaneous localization and mapping (SLAM) framework. The range data is obtained by echosounder and sidescanning sonar images from side scan sonar module for UWSim. For the feature, we used the A-KAZE feature for the SSS image matching and adjusting the relative robot pose by SSS bundle adjustment (BA) with Ceres solver. We use BA for the loop closure constraint of pose-graph SLAM. We used the Incremental Smoothing and Mapping (iSAM) to optimize the graph. The optimized trajectory was compared against the dead reckoning (DR).

• 제어로봇시스템학회논문지
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• 제14권2호
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• pp.133-140
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• 2008

In this paper, a small and effective attitude estimation system for a humanoid robot was developed. Four small inertial sensors were packed and used for inertial measurements(3D accelerometer and three 1D gyroscopes.) An effective 3D pose estimation algorithm for low cost DSP using an extended Kalman filter was developed and evaluated. The 3D pose estimation algorithm has a very simple structure composed by 3 modules of a linear acceleration estimator, an external acceleration detector and an pseudo-accelerometer output estimator. The algorithm also has an effective switching structure based on probability and simple feedback loop for the extended Kalman filter. A special test equipment using linear motor for the testing of the 3D pose sensor was developed and the experimental results showed its very fast convergence to real values and effective responses. Popular DSP of TMS320F2812 was used to calculate robot's 3D attitude and translated acceleration, and the whole system were packed in a small size for humanoids robots. The output of the 3D sensors(pitch, roll, 3D linear acceleration, and 3D angular rate) can be transmitted to a humanoid robot at 200Hz frequency.