• 로봇학회논문지
• /
• 제4권4호
• /
• pp.298-304
• /
• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• 한국산업정보학회논문지
• /
• 제19권5호
• /
• pp.57-68
• /
• 2014

로봇 개발에서 하드웨어를 개발한 후에 테스트하면 많은 시행착오를 겪게 되어 많은 비용이 소요된다. 로봇이 현장과 동일한 로봇 시뮬레이션을 사용하여 로봇을 개발하면 소프트웨어 및 하드웨어의 병행 개발 및 테스트를 통해 개발결과를 예측할 수 있고 비용을 절감할 수 있다. 로봇 시뮬레이션 플랫폼인 마이크로소프트의 로보틱스 개발자 스튜디오(MSRDS)는 하드웨어 로봇이 없이도 기본적인 로봇 프로그래밍을 할 수 있는 시뮬레이션 로봇과 환경을 제공한다. 본 논문에서는 MSRDS에서 LRF센서, Bumper센서, IR센서, Sonar센서의 성능을 비교분석하기 위하여 미로 찾기를 수행한다. 센서의 성능을 분석하기 위하여 동일한 조건으로 실험한다. 4가지의 센서중에서 LRF센서가 주행시간, 방향전환횟수, 장애물 충돌횟수면에서 우수한 성능을 보인 반면에 범퍼센서는 가장 성능이 낮았다. IR센서와 Sonar센서는 방향 전환횟수면에서 LRF센서 보다는 낮은 성능을 보였다.

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

• 한국정보전자통신기술학회논문지
• /
• 제13권5호
• /
• pp.377-388
• /
• 2020

본 논문에서는 고정된 장애물이 포함된 글로벌 맵 환경에서 LRF 센서만을 가진 자율이동 로봇이 이동장애물을 회피하기 위한 알고리즘을 제안한다. 우선 이동장애물을 회피하기 위해 LRF 거리 센서 데이터와 글로벌 맵을 이용하여 이동장애물을 추출한다. 추출된 이동장애물과 자율이동 로봇의 상대적인 벡터 성분의 합을 이용해 타원 형태의 안전반경을 생성한다. 생성된 안전반경을 고려하여 자율이동 로봇이 이동장애물을 회피하고 목적지에 도착할 수 있도록 한다. 제안된 알고리즘을 검증하기 위해 정량적인 분석 방법을 사용하여 기존 알고리즘과 비교하고 분석한다. 분석 방법은 이동장애물이 없을 때를 기준으로 제안된 알고리즘과 기존의 알고리즘의 경로의 길이와 주행 시간을 비교한다. 제안된 알고리즘은 이동장애물의 상대적 속도와 방향을 고려하여 회피할 수 있어서 경로와 주행 시간 모두 기존의 알고리즘보다 높은 성능을 보인다.

• 제어로봇시스템학회논문지
• /
• 제18권6호
• /
• pp.601-607
• /
• 2012

The recognition of elevator door is needed for mobile service robots to moving between floors in the building. This paper proposed the sensor fusion approach using LRF (Laser Range Finder) and camera to solve the problem. Using the laser scans by the LRF, we extract line segments and detect candidates as the elevator door. Using the image by the camera, the door candidates are verified and selected as real door of the elevator. The outliers are filtered through the verification process. Then, the door state detection is performed by depth analysis within the door. The proposed method uses extrinsic calibration to fuse the LRF and the camera. It gives better results of elevator door recognition compared to the method using LRF only.

• 제어로봇시스템학회논문지
• /
• 제17권10호
• /
• pp.995-1005
• /
• 2011

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-Update Extended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations or military services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor local localization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data is used for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, and estimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation is finally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor data efficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposed algorithm is revealed via experiments.

• 제어로봇시스템학회논문지
• /
• 제14권7호
• /
• pp.635-641
• /
• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.

• 로봇학회논문지
• /
• 제6권3호
• /
• pp.255-262
• /
• 2011

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

• 로봇학회논문지
• /
• 제7권2호
• /
• pp.113-119
• /
• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2009년도 춘계학술대회 논문집
• /
• pp.295-296
• /
• 2009