• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 춘계학술대회 논문집
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• pp.661-662
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• 2008

• 제어로봇시스템학회논문지
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• 제21권3호
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• pp.265-275
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• 2015

This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.

• 대한기계학회논문집A
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• 제30권9호
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• pp.1124-1133
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• 2006

This paper addresses the localization and navigation problem using invisible two dimensional barcodes on the floor. Compared with other methods using natural/artificial landmark, the proposed localization method has great advantages in cost and appearance, since the location of the robot is perfectly known using the barcode information after the mapping is finished. We also propose a navigation algorithm which uses the topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls and many static obstacles. The proposed algorithm also has an advantage that errors occurred in each node are mutually independent and can be compensated exactly after some navigation using barcode. Simulation and experimental results. were performed to verify the algorithm in the barcode environment, and the result showed an excellent performance. After mapping, it is also possible to solve the kidnapped case and generate paths using topological information.

• 한국전자통신학회논문지
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• 제8권4호
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• pp.575-582
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• 2013

본 논문에서는 로봇 위치 예측 기반을 둔 ICP 알고리즘을 이용한 지도를 작성한다. 제안한 방법은 데드레크닝으로 로봇 위치를 예측하고 ICP 알고리즘으로 지도를 작성 한다. 기존 방법은 기준데이터와 새 데이터의 센서 값만을 이용하여 로봇의 위치와 지도를 작성한다. 기존 방법은 현재 데이터와 기준 값과의 간격의 차이가 조금만 멀어져도 보정하기가 어렵다. 하지만 제안한 방법으로 지도를 작성할 경우에는 지도의 틀어진 정도가 기존 방법으로는 지도를 보정 할 수 없지만 제안한 방법은 지도 보정을 할 수 있음을 실제 실험을 통해 나타내었다.

• 제어로봇시스템학회논문지
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• 제14권9호
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• pp.893-897
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• 2008

To achieve the challenges of low-cost MEMS gyros for the precise self-localization of mobile robots, this paper examines an effective method of minimizing the drift on the heading angle that relies solely on integration of rate signals from a gyro. The main idea of the proposed approach is to use wavelet de-noising filter in order to reduce random noise which affects short-term performances. The proposed method was applied to Epson XV3500 gyro and the performances are verified by the comparisons with an existing commercial gyro module of vacuum cleaning robots.

• 전자공학회논문지SC
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• 제48권1호
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• pp.55-60
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• 2011

이동로봇의 동시 위치인식 및 지도작성 (Simultaneous Localization And Mapping, SLAM) 에서 가장 기본이 되는 알고리즘은 확장 칼만 필터 SLAM(Extended Kalman Filter SLAM, EKF-SLAM)이다. 하지만 칼만 필터를 사용할 때, 시스템 설계자는 외부 입력에 대한 시스템적 특성과 외부 노이즈의 확률적 모델을 알고 있어야 하나, 실제 환경에서는 이를 정확히 파악할 수 없는 한계가 있다. 이에, 칼만 필터를 불확실성이 심한 실제 환경에 적용할 경우 내부 변수의 변화에 민감하게 반응하거나, 필터의 수학적 일관성이 지켜지지 않거나 또는 부정확한 상태 변수값을 추정하기도 한다. 이에 비해 $H_{\infty}$ 필터는 외부 모델에 대한 상세한 정보가 없을지라도 강인하게 상태를 예측할 수 있다는 장점을 가지고 있다. 본 논문에서는 이러한 $H_{\infty}$ 필터의 특성이 이용로봇의 SLAM 알고리즘의 성능 향상에 도움이 될 것이라는 아이디어에 착안하여 $H_{\infty}$ 필터에 가번한 SLAM 알고리즘을 제안하고 이를 모의 실험에 적용해 보았다. 이를 통해 불확실성이 큰 환경에서는 제안된 알고리즘이 기존의 EKF-SLAM에 비해 다소 우수한 예측 성능을 보임을 확인할 수 있었다.

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

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.544-547
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• 1997

In this paper, we propose a dynamic localization method using a rotating sonar and a map. The proposed method is implemented by using extended Kalman filter. The state equation is based on the encoder propagation model and the encoder error model, and the measurement equation is a map-based measurement equation using a rotating sonar sensor. By utilizing sonar beam characteristics, map-based measurements are updated while AMR is moving continuously. By modeling and estimating systematic errors of a differential encoder, the position is successfully estimated even the interval of the map-based measurement. Monte-Carlo simulation shows that the proposed global position estimator has the performance of a few millimeter order in position error and of a few tenth degrees in heading error and of compensating systematic errors of the differential encoder well.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2450-2452
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• 2004

Map building is essential to a mobile robot navigation system. Localization and path planning methods depend on map building strategies. A topological map is commonly constructed using the GVG(Generalized Voronoi Graph). The advantage of the GVG based topological map is compactness. But the GVG method have many difficulties because it consists of collision-free path. In this paper, we proposed an extended map building method, the AVG (Areal Voronoi Graph) based topological map. The AVG based topological map consists of collision-free area. This feature can improve map building, localization and path planning performance.

• 제어로봇시스템학회논문지
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• 제4권6호
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• pp.747-752
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• 1998

Ultrasonic sensors have been widely used to recognize the working environment for a mobile robot. However, their intrinsic problems, such as specular reflection, wide beam angle, and slow propagation velocity, require an excessive number of sensors to be integrated for achieving the sensing goal. This paper proposes a new measurement scheme which uses only two sets of ultrasonic sensors to determine the location and the type of a target surface. By measuring the time difference between the returned signals from the target surface, which are generated by two transmitters with 1 ㎳ difference, it classifies the type and determines the size of the target surface. Since the proposed sensor system uses only two sets of ultrasonic sensors to recognize and localize the target surface, it significantly simplifies the sensing system and reduces the signal processing time so that the working environment can be recognized in real time.