Underwater Navigation of AUVs Using Uncorrelated Measurement Error Model of USBL

Lee, Pan-Mook,Park, Jin-Yeong,Baek, Hyuk,Kim, Sea-Moon,Jun, Bong-Huan,Kim, Ho-Sung,Lee, Phil-Yeob Korean Society of Ocean Engineers 2022 韓國海洋工學會誌 Vol.36 No.5

This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.

Performance Enhancement Step for Motion Estimation via Feature-based Image Matching

Keita Miyaura,Armagan Elibol,Nak Young Chong 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11

Most of the complicated and sophisticated tasks in visual robotics applications usually build upon the image matching step as matching images of the same scene can provide important information (e.g., camera motion). Image matching is generally done via extracting and matching some distinctive points via their feature vectors. This procedure generates some mismatched points due to imperfections. Mismatched points are called outliers and identified via probabilistic methods. Since the probabilistic methods work iteratively, they generally occupy a large portion of the computational cost of the whole image matching pipeline. In this paper, we present a simple yet efficient algorithm that is employed for eliminating the outliers aiming at reducing the total number of iterations needed in the probabilistic methods. Our method is motivated by the common way of visualizing the established matches among images. We tile images together and search for parallel lines connecting correspondences. We present extensive computational and comparative experiments using both simulated data involving along with real images and using a real dataset.

A Fast and Efficient Image Registration Algorithm using Outlier Rejection Technique based on Subimage

양동원,김도종 제어·로봇·시스템학회 2013 International Journal of Control, Automation, and Vol.11 No.4

Image registration is a process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and by different sensors. It geometrically aligns two images, the reference and sensed image. In this paper, a fast and efficient image registration algorithm is proposed for IDS (Intruder Detection System). To reduce a calculation time, outlier rejection method based on uniformity, entropy and subimage is used. An edge tapering method is applied to alleviate a boundary effect of a subimage. And it is shown that the proposed algorithm improves the accuracy and calculation time effectively.

동적 환경에 강인한 장면 인식 기반의 로봇 자율 주행

김정호,권인소 한국로봇학회 2008 로봇학회 논문지 Vol.3 No.3

Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To solve these problems, we propose a novel navigation strategy which uses fast motion estimation and a practical scene recognition scheme preparing the kidnapping problem, which is defined as the problem of re-localizing a mobile robot after it is undergone an unknown motion or visual occlusion. This algorithm is based on motion estimation by a camera to plan the next movement of a robot and an efficient outlier rejection algorithm for scene recognition. Experimental results demonstrate the capability of the vision-based autonomous navigation against dynamic environments.

Adaptive robust estimation of affine parameters from block motion vectors

Jang, Seok-Woo,Pomplun, Marc,Kim, Gye-Young,Choi, Hyung-Il Elsevier 2005 Image and vision computing Vol.23 No.14

<P><B>Abstract</B></P><P>In this paper, we propose an affine parameter estimation algorithm from block motion vectors for extracting accurate motion information with the assumption that the undergoing motion can be characterized by an affine model. The motion may be caused either by a moving camera or a moving object. The proposed method first extracts motion vectors from a sequence of images by using size-variable block matching and then processes them by adaptive robust estimation to estimate affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a continuous weight function based on a Sigmoid function. During the estimation process, we tune the Sigmoid function gradually to its hard-limit as the errors between the model and input data are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. Experimental results show that the suggested approach is very effective in estimating affine parameters reliably.</P>

수중로봇 위치추정을 위한 베이시안 필터 방법의 실현과 거리 측정 특성 분석

노성우,고낙용,김태균 한국로봇학회 2014 로봇학회 논문지 Vol.9 No.1

This paper verifies the performance of Extended Kalman Filter(EKF) and MCL(Monte Carlo Localization) approach to localization of an underwater vehicle through experiments. Especially, the experiments use acoustic range sensor whose measurement accuracy and uncertainty is not yet proved. Along with localization, the experiment also discloses the uncertainty features of the range measurement such as bias and variance. The proposed localization method rejects outlier range data and the experiment shows that outlier rejection improves localization performance. It is as expected that the proposed method doesn’t yield as precise location as those methods which use high priced DVL(Doppler Velocity Log), IMU(Inertial Measurement Unit), and high accuracy range sensors. However, it is noticeable that the proposed method can achieve the accuracy which is affordable for correction of accumulated dead reckoning error, even though it uses only range data of low reliability and accuracy.

최적화 기법을 사용한 실내 이동 로봇의 위치 인식

한준희(Jun Hee Han),고낙용(Nak Yong Ko) 한국지능시스템학회 2016 한국지능시스템학회논문지 Vol.26 No.4

본 논문은 실내 주행 로봇의 위치 추정을 위해 최적화 기법을 적용한 방법에 대해 기술한다. 주행 로봇의 위치 추정에 사용되는 베이지안 필터 방법의 경우는 측정값과 환경 요소에 대한 불확실성을 고려하기위해 사용하는 조절 파라미터에 따라 추정성능이 달라진다. 또한 로봇동작 및 센서 측정 모델의 비선형성에 의하여 성능이 저하될 수 있다. 최적화 기법은 조절 파라미터가 적고 모델의 비선형성의 영향을 적게 받는다. 본 연구에서는 최적화 기법의 위치 추정 활용성을 보이기 위해 최적화 방법에 의한 추정성능과 EKF방법에 의한 추정 성능을 비교한다. 사용한 측정 센서는 초음파 위성 시스템(USAT, Ultrasonic Satellites system)으로서 4개의 비컨으로부터 로봇까지의 거리를 측정한다. 측정값의 비정상 오차를 제거하기 위하여 마할라노비스 거리(Mahalanobis Distance)를 이용한다. 최적화 기법은 거리 측정값을 사용하여 목적함수를 설계하고 반복계산을 통해 위치의 최적 값을 찾는다. 반복 수행을 위한 초기 위치를 베이시안 필터 방법을 통하여 적절히 설정함으로서 제안된 방법은 위치 추정 성능을 향상시키고 실행 시간을 단축시킬 수 있다. This paper proposes a method that utilizes optimization approach for localization of an indoor mobile robot. Bayesian filters which have been widely used for localization of a mobile robot use many control parameters to take the uncertainties in measurement and environment into account. The estimation performance depends on the selection of these parameter values. Also, the performance of the Bayesian filters deteriorate as the non-linearity of the motion and measurement increases. On the other hand, the optimization approach uses fewer control parameters and is less influenced by the non-linearity than the Bayesian methods. This paper compares the localization performance of the proposed method with the performance of the extended Kalman filter to verify the feasibility of the proposed method. Measurements of ranges from beacons of uraltsonic satellite to the robot are used for localization. Mahaanlobis distance is used for detection and rejection of outlier in the measurements. The optimization method sets performance index as a function of the measured range values, and finds the optimized estimation of the location through iteration. The method can improve the localization performance and reduce the computation time in corporation with Bayesian filter which provides proper initial location for the iteration.

Deep ORFCM 알고리즘을 이용한 초음파영상에의 충수염 추출

김재영(Jae-Young Kim),김광백(Kwang-Beak Kim) 한국정보통신학회 2019 한국정보통신학회 종합학술대회 논문집 Vol.23 No.2

잔차 거리를 사용한 특징점 매칭에서의 이상점 제거

배병조(Byeong-Jo Bae),박종승(Jong-Seung Park) 한국정보기술학회 2010 한국정보기술학회논문지 Vol.8 No.11

잡음이 심한 패널 모듈 영상에서의 에지 선 검출 방법

이승목(Seung-Mok Lee),김영형(Young-Hyung Kim),임재권(Jae-Kwon Eem) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.7