• 한국정밀공학회지
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• 제28권10호
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• pp.1174-1180
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• 2011

Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.

• 로봇학회논문지
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• 제1권1호
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• pp.25-35
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• 2006

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home -like environment.

• 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).

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.185-186
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• 2010

• 전자공학회논문지
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• 제54권3호
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• pp.108-118
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• 2017

본 논문은 수중 로봇 항법에 사용하기 위한 영상 소나 기반 SLAM (simultaneous localization and mapping) 방법을 제안하고, 성능 평가를 위해 실제 로봇에 탑재하여 실험한 내용을 소개한다. 일반적인 수중 항법은 관성 센서에서 출력되는 정보를 바탕으로 로봇의 위치 및 자세(x,y,z,${\phi}$,${\theta}$,${\psi}$)를 추정한다. 하지만, 장시간 주행할 경우 위치 오차의 누적으로 인하여 정확도가 감소하게 된다. 이에 본 논문에서는 영상 소나로부터 얻을 수 있는 외부 정보를 바탕으로 관성 항법의 위치 추정 성능을 높이고 지도 작성을 수행할 수 있는 SLAM 방법을 제안하고자 한다. 영상 소나를 위한 인공 표식물과 확률 기반 물체 인식 구조를 통해 인공 표식물의 인식 성능을 높이고, 이를 통해 얻게 된 인공 표식물의 위치 정보를 활용하여 관성 항법의 누적 오차를 줄이고자 한다. 항법 알고리즘으로는 확장형 칼만 필터(Extended Kalman Filter, EKF)를 적용하여 로봇의 위치 및 자세를 추정하고 지도를 작성한다. 제안한 방법은 선박해양플랜트연구소에서 보유 중인 수중 로봇 'yShark'에 탑재하여 대형 수조에서 실시간 검증을 수행하였다.

• 로봇학회논문지
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• 제9권4호
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• pp.232-241
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• 2014

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.1607-1608
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• 2011

• 한국해양공학회지
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• 제23권2호
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• pp.58-68
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• 2009

The increased use of unmanned underwater vehicles (UUV) has led to the development of alternative navigational methods that do not employ acoustic beacons and dead reckoning sensors. This paper describes a simultaneous localization and mapping (SLAM) scheme that uses range sonars mounted on a small UUV. A SLAM scheme is an alternative navigation method for measuring the environment through which the vehicle is passing and providing the relative position of the UUV. A technique for a SLAM algorithm that uses several ranging sonars is presented. This technique utilizes an unscented Kalman filter to estimate the locations of the UUV and surrounding objects. In order to work efficiently, the nearest neighbor standard filter is introduced as the data association algorithm in the SLAM for associating the stored targets returned by the sonar at each time step. The proposed SLAM algorithm was tested by experiments under various three degrees of freedom motion conditions. The results of these experiments showed that the proposed SLAM algorithm was capable of estimating the position of the UUV and the surrounding objects and demonstrated that the algorithm will perform well in various environments.

• 로봇학회논문지
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• 제13권2호
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• pp.79-85
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• 2018

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.

• 한국항행학회논문지
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• 제18권2호
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• pp.134-141
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• 2014

이동로봇의 주행에는 주로 바퀴 엔코더, 비전, 초음파, 레이저 센서가 많이 사용된다. 바퀴의 엔코더는 추측항법으로 시간에 따라 오차가 누적되기 때문에 단독 사용으로는 정확한 로봇의 위치를 계산할 수가 없다. 비전 센서는 풍부한 정보를 제공하지만 정보추출에 시간이 많이 소요되고, 초음파 센서는 거리정보의 정확도가 떨어지기 때문에 항행에 사용하기에는 어려움이 있다. 반면 레이저 센서는 비교적 정확한 거리정보를 제공하여 주므로 주행 센서로 사용하기 적합하다. 본 논문에서는 레이저 거리계에서 각도를 추출하는 방법을 제안하고 칼만 필터를 사용하여 레이저 거리계에서 추출한 거리 및 각도와 바퀴 엔코더에서 추출한 거리 및 각도에 대한 정합을 수행한다. 일반적으로 레이저 거리계 사용시 특징점 하나를 사용한 경우에 그 특징점이 변하거나 새로운 특징점으로 이동할 때 오차가 커질 수가 있다. 이를 보완하기 위해 이동 로봇의 주행 시 레이저 스캐너에서 두 개의 특징점들을 사용하는 방법을 사용하여 이동 로봇의 항법 성능이 향상됨을 보인다.