• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.429-435
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• 2011

This paper presents a sensor fusion-based estimation of heading and a Bezier curve-based motion planning for unmanned ground vehicle. For the vehicle to drive itself autonomously and safely, it should estimate its pose with sufficient accuracy in reasonable processing time. The vehicle should also have a path planning algorithm that enables to adapt to various situations on the road, especially at intersections. First, we address a sensor fusion-based estimation of the heading of the vehicle. Based on extended Kalman filter, the algorithm estimates the heading using the GPS, IMU, and wheel encoders considering the reliability of each sensor measurement. Then, we propose a Bezier curve-based path planner that creates several number of path candidates which are described as Bezier curves with adaptive control points, and selects the best path among them that has the maximum probability of passing through waypoints or arriving at target points. Experiments under various outdoor conditions including at intersections, verify the reliability of our algorithm.

• 한국지능시스템학회논문지
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• 제19권2호
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• pp.168-173
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• 2009

본 논문은 수중 차의 차속도와 프로펠러 각속도를 추정하는 문제를 다룬다. 계단식 관측기는 측정된 위치 값으로부터 속도값 추정을 위해 사용한다. 고이득 관측기(high-gain observer)에 전형적으로 생기는 전형적인 문제를 없애기 위하여 계단식 구조의 관측기가 설계되었다. 고이득 관측기처럼 시스템 다이나믹스와 파라미터로부터 무관하게 설계할 수 있으며 단순한 구조를 가진다 관측기의 첫 단계에서 출력 값이 추정되고 측정된 출력의 1계 미분 값이 관측기의 두 번째 단계를 통해 추정된다. 또한 출력의 n 번째 미분 값은 관측기의 n+1 번째 단계에서 추정된다. 제안된 관측기가 전체 점근적 안정도를 보장함을 보여준다. 시뮬레이션 결과는 기존의 고이득 관측기에 비해 제안된 관측기가 수중 차의 차속도와 프로펠러 각속도를 더 우수하게 추정함을 보여준다.

• 로봇학회논문지
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• 제19권2호
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• pp.188-195
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• 2024

This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a neural network-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. At the same time, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.

• Journal of Positioning, Navigation, and Timing
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• 제2권1호
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• pp.81-89
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• 2013

Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

• 로봇학회논문지
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• 제11권2호
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• pp.51-59
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• 2016

In this paper we present (1) analysis of imaging sonar measurement for two-view relative pose estimation of an autonomous vehicle and (2) bundle adjustment and 3D reconstruction method using imaging sonar. Sonar has been a popular sensor for underwater application due to its robustness to water turbidity and visibility in water medium. While vision based motion estimation has been applied to many ground vehicles for motion estimation and 3D reconstruction, imaging sonar addresses challenges in relative sensor frame motion. We focus on the fact that the sonar measurement inherently poses ambiguity in its measurement. This paper illustrates the source of the ambiguity in sonar measurements and summarizes assumptions for sonar based robot navigation. For validation, we synthetically generated underwater seafloor with varying complexity to analyze the error in the motion estimation.

• 제어로봇시스템학회논문지
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• 제21권4호
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• pp.349-355
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• 2015

Underwater robots generally show better performances for tasks than humans under certain underwater constraints such as. high pressure, limited light, etc. To properly diagnose in an underwater environment using remotely operated underwater vehicles, it is important to keep autonomously its own position and orientation in order to avoid additional control efforts. In this paper, we propose an efficient method to assist in the operation for the various disturbances of a remotely operated vehicle for the diagnosis of underwater structures. The conventional AHRS-based bearing estimation system did not work well due to incorrect measurements caused by the hard-iron effect when the robot is approaching a ferromagnetic structure. To overcome this drawback, we propose a sensor fusion algorithm with the camera and AHRS for estimating the pose of the ROV. However, the image information in the underwater environment is often unreliable and blurred by turbidity or suspended solids. Thus, we suggest an efficient method for fusing the vision sensor and the AHRS with a criterion which is the amount of blur in the image. To evaluate the amount of blur, we adopt two methods: one is the quantification of high frequency components using the power spectrum density analysis of 2D discrete Fourier transformed image, and the other is identifying the blur parameter based on cepstrum analysis. We evaluate the performance of the robustness of the visual odometry and blur estimation methods according to the change of light and distance. We verify that the blur estimation method based on cepstrum analysis shows a better performance through the experiments.

• 반도체디스플레이기술학회지
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• 제10권2호
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• 전기전자학회논문지
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• 제25권4호
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• pp.762-765
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• 2021

본 논문에서는 자동차의 위치 및 자세 추정에 사용되는 칼만 필터 가속기를 내장한 차량용 ECU(electronic control unit)를 설계하고 구현하였다. 프로세서 코어는 RISC-V를 사용하였으며 칼만 필터의 행렬 연산을 수행하는 가속기, 차량 내 통신에 사용되는 CAN(controller area network) 제어기, 센서 연결에 사용되는 LIN(local interconnect network) 제어기를 내장하였다. 칼만 필터 연산은 시간 업데이트와 측정 업데이트의 두 단계로 나뉘며 시간 업데이트 단계에서는 현재 상태변수와 오차 공분산을 예측하고 측정 업데이트 단계에서는 입력값을 받아 칼만 이득을 계산하여 값을 보정한다. 보통 소프트웨어에서는 곱셈에 부동소숫점 연산을 사용하지만 본 논문에서는 하드웨어 면적을 줄이기 위해 정밀도 분석을 고려한 고정소숫점 곱셈기를 사용하였다. 설계된 ECU는 Verilog HDL을 이용하여 검증하였으며 28nm 실리콘 공정으로 구현하였다. 28nm 실리콘 공정으로 구현하였을 때 동작 주파수는 100MHz, 면적은 0.37mm², 게이트 수는 76만 게이트였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권10호
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• pp.569-574
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• 2003

Computer vision system is broadly adapted like as autonomous vehicle system, product line inspection, etc., because it has merits which can deal with environment flexibly. However, for applying it for that industry, it has to clear the problem that recognize position parameter of itself. So that computer vision system stands in need of camera calibration to solve that. Camera calibration consists of the intrinsic parameter which describe electrical and optical characteristics and the extrinsic parameter which express the pose and the position of camera. And these parameters have to be reorganized as the environment changes. In traditional methods, however, camera calibration was achieved at off-line condition so that estimation of parameters is in need again. In this paper, we propose a method to the calibration of camera using line correspondence in image sequence varied environment. This method complements the corresponding errors of the point corresponding method statistically by the extraction of line. The line corresponding method is strong by varying environment. Experimental results show that the error of parameter estimated is within 1% and those is effective.

• 한국군사과학기술학회지
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• 제14권2호
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• pp.271-280
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• 2011

This paper presents a navigation system based on the tactical communication and vision system in outdoor environments which is applied to unmanned robot for perimeter surveillance operations. GPS errors of robot are compensated by the reference station of C2(command and control) vehicle and WiBro(Wireless Broadband) is used for the communication between two systems. In the outdoor environments, GPS signals can be easily blocked due to trees and buildings. In this environments, however, vision system is very efficient because there are many features. With the feature MAP around the operation environments, the robot can estimate the position by the image matching and pose estimation. In the navigation system, thus, operation modes is switched by navigation manager according to some environment conditions. The experimental results show that the unmanned robot can estimate the position very accurately in outdoor environment.