• 전자공학회논문지
• /
• 제54권3호
• /
• pp.108-118
• /
• 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
• /
• 제1권1호
• /
• pp.25-35
• /
• 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.

• 한국전자통신학회논문지
• /
• 제15권2호
• /
• pp.237-244
• /
• 2020

본 논문은 2차원 공간에서 SLAM(: Simultaneous Localization and Mapping)의 구현을 설명한다. 본 논문에서 사용한 방법은 불변량이라고 하는 변수가 일정하게 유지 될 때 변환된 변수가 선형 공간을 구성하도록 상태 변수와 측정 변수를 변환하는 IEKF(: Invariant extended Kalman filter)를 사용한다. 따라서, IEKF는 불변량이 일정하게 유지되는 경우 수렴을 보장한다. 제안된 IEKF 접근법 중 변환을 하는 과정에서는 리군(Lie group) 행렬을 사용한다. 이 방법은 시뮬레이션을 통해 테스트 되었으며 결과는 선형 칼만 필터의 경우와 마찬가지로 칼만 이득이 일정하다는 것을 보여준다. 즉, 시뮬레이션 결과 이동체의 추정된 위치와 검출된 물체들 사이의 일관성을 보였다.

• 로봇학회논문지
• /
• 제11권3호
• /
• pp.146-155
• /
• 2016

Localization of underwater vehicle is essential to use underwater robotic systems for various applications effectively. For this purpose, this paper presents a method of two-dimensional SLAM for underwater vehicles equipped with two hydrophones. The proposed method uses directional angles for underwater acoustic sources. A target signal transmitted from acoustic source is extracted using band-pass filters. Then, directional angles are estimated based on Bayesian process with generalized cross-correlation. The acquired angles are used as measurements for EKF-SLAM to estimate both vehicle location and locations of acoustic sources. Through these processes, the proposed method provides reliable estimation for two dimensional locations of underwater vehicles. Experimental results demonstrate the performance of the proposed method in a real sea environment.

• 제어로봇시스템학회논문지
• /
• 제15권2호
• /
• pp.218-224
• /
• 2009

This paper proposes an effective SLAM based on redundant extended Kalman filter for robot navigation in an irregular surface to enhance the accuracy of robot's pose. To establish an accurate model of a caterpillar type robot is very difficult due to the mechanical complexity of the system which results in highly nonlinear behavior. In addition, for robot navigation on an irregular surface, its control suffers from the uncertain pose of the robot heading closely related to the condition of the floor. We show how this problem can be overcome by the proposed approach based on redundant extended Kalman filter through some computer simulation results.

• 한국정밀공학회지
• /
• 제30권2호
• /
• pp.177-184
• /
• 2013

This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

• 제어로봇시스템학회논문지
• /
• 제19권8호
• /
• pp.667-675
• /
• 2013

This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

• 제어로봇시스템학회논문지
• /
• 제12권6호
• /
• pp.585-593
• /
• 2006

In this paper, we propose a vision-based simultaneous localization and map-building (SLAM) algorithm. SLAM problem asks the location of mobile robot in the unknown environments. Therefore, this problem is one of the most important processes of mobile robots in the outdoor operation. To solve this problem, Extended Kalman filter (EKF) is widely used. However, this filter requires computational power (${\sim}O(N)$, N is the dimension of state vector). To reduce the computational complexity, we applied compressed extended Kalman filter (CEKF) to stereo image sequence. Moreover, because the mobile robots operate in the outdoor environments, we should estimate full d.o.f.s of mobile robot. To evaluate proposed SLAM algorithm, we performed the outdoor experiments. The experiment was performed by using new wheeled type mobile robot, Robhaz-6W. The performance results of CEKF SLAM are presented.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.174-179
• /
• 2003

Through the simultaneous localization and map building (SLAM) technique, a robot can create maps about its unknown environment while it continuously localizes its position. Grid maps and feature maps have been widely used for SLAM together with application of probability methods and POMDP (partially observed Markov decision process). But this approach based on grid maps suffers from enormous computational burden. Topological maps, however, have drawn more attention these days because they are compact, provide natural interfaces, and are easily applicable to path planning in comparison with grid maps. Some topological SLAM techniques like GVG (generalized Voronoi diagram) were introduced, but it enables the robot to decide only whether the current position is part of GVG branch or not in the GVG algorithm. In this paper, therefore, to overcome these problems, we present a method for updating a global topological map from the local topological maps. These local topological maps are created through a labeled Voronoi diagram algorithm from the local grid map built based on the sensor information at the current robot position. And the nodes of a local topological map can be utilized as the features of the environment because it is robust in light of visibility problem. The geometric information of the feature is applied to the extended Kalman filter and the SLAM in the indoor environment is accomplished. A series of simulations have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can be applied relatively well.

• 로봇학회논문지
• /
• 제7권2호
• /
• pp.120-128
• /
• 2012

The loop closure problem is one of the most challenging issues in the vision-based simultaneous localization and mapping community. It requires the robot to recognize a previously visited place from current camera measurements. While the loop closure often relies on visual bag-of-words based on point features in the previous works, however, in this paper we propose a line-based method to solve the loop closure in the corridor environments. We used both the floor line and the anchored vanishing point as the loop closing feature, and a two-step loop closure algorithm was devised to detect a known place and perform the global pose correction. We propose an anchored vanishing point as a novel loop closure feature, as it includes position information and represents the vanishing points in bi-direction. In our system, the accumulated heading error is reduced using an observation of a previously registered anchored vanishing points firstly, and the observation of known floor lines allows for further pose correction. Experimental results show that our method is very efficient in a structured indoor environment as a suitable loop closure solution.