• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.87-93
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• 2019

This paper presents a 6-DOF relocalization using a 3D laser scanner and a monocular camera. A relocalization problem in robotics is to estimate pose of sensor when a robot revisits the area. A deep convolutional neural network (CNN) is designed to regress 6-DOF sensor pose and trained using both RGB image and 3D point cloud information in end-to-end manner. We generate the new input that consists of RGB and range information. After training step, the relocalization system results in the pose of the sensor corresponding to each input when a new input is received. However, most of cases, mobile robot navigation system has successive sensor measurements. In order to improve the localization performance, the output of CNN is used for measurements of the particle filter that smooth the trajectory. We evaluate our relocalization method on real world datasets using a mobile robot platform.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.508-518
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• 2016

In this paper, we propose a method combining an accelerometer with a cross structured light system to estimate the golf green slope. The cross-line laser provides two laser planes whose functions are computed with respect to the camera coordinate frame using a least square optimization. By capturing the projections of the cross-line laser on the golf slope in a static pose using a camera, two 3D curves’ functions are approximated as high order polynomials corresponding to the camera coordinate frame. Curves’ functions are then expressed in the world coordinate frame utilizing a rotation matrix that is estimated based on the accelerometer’s output. The curves provide some important information of the green such as the height and the slope’s angle. The curves estimation accuracy is verified via some experiments which use OptiTrack camera system as a ground-truth reference.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.2-100
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• 2002

In this paper we propose a new visual servoing method for door opening with mobile manipulator. We use an eye-to-hand system that stereo camera is mounted on mobile platform, and adopt the position-based method. The previous methods for door opening mostly used eye-in-hand system with mono camera and required predefined knowledge such as radius and position about door grip, which was mainly caused by using mono cam era. This is also a severe constraint for pursuing general-purpose algorithm for door opening. For overcoming such drawback, we use stereo camera and suggest a new method that detect the door grip and estimate its pose from stereo depth information without predefined knowledge. Al...

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.231-233
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• 2007

In this paper we present a new visual approach for the robust bin-picking in a two-step concept for a vision driven automatic handling robot. The technology described here is based on two types of sensors: 3D laser scanner and CCD video camera. The geometry and pose(position and orientation) information of bin contents was reconstructed from the camera and laser sensor. these information can be employed to guide the robotic arm. A new thinning algorithm and constrained hough transform method is also explained in this paper. Consequently, the developed bin-picking demonstrate the successful operation with 3D hole object.

• Korean Journal of Computational Design and Engineering
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• v.22 no.1
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• pp.89-99
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• 2017

In order to make a virtual endoscopy system effective for exploring the interior of the 3D model of a human organ, it is necessary to generate an accurate navigation path located inside the 3D model and to obtain consistent camera position and pose estimation along the path. In this paper, we propose an approach to virtual navigation of blood vessels, which makes proper use of orthogonal contours and skeleton curves. The approach generates the orthogonal contours and the skeleton curves from the 3D mesh model and its voxel model, all of which represent the blood vessels. For a navigation zone specified by two nodes on the skeleton curves, it computes the shortest path between the two nodes, estimates the positions and poses of a virtual camera at the nodes in the navigation zone, and interpolates the positions and poses to make the camera move smoothly along the path. In addition to keyboard and mouse input, intuitive hand gestures determined by the Leap Motion SDK are used as user interface for virtual navigation of the blood vessels. The proposed approach provides easy and accurate means for the user to examine the interior of 3D blood vessels without any collisions between the camera and their surface. With a simple user study, we present illustrative examples of applying the approach to 3D mesh models of various blood vessels in order to show its quality and usefulness.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.569-574
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• 2003

Computer vision system is broadly adapted like as autonomous vehicle system, product line inspection, etc., because it has merits which can deal with environment flexibly. However, for applying it for that industry, it has to clear the problem that recognize position parameter of itself. So that computer vision system stands in need of camera calibration to solve that. Camera calibration consists of the intrinsic parameter which describe electrical and optical characteristics and the extrinsic parameter which express the pose and the position of camera. And these parameters have to be reorganized as the environment changes. In traditional methods, however, camera calibration was achieved at off-line condition so that estimation of parameters is in need again. In this paper, we propose a method to the calibration of camera using line correspondence in image sequence varied environment. This method complements the corresponding errors of the point corresponding method statistically by the extraction of line. The line corresponding method is strong by varying environment. Experimental results show that the error of parameter estimated is within 1% and those is effective.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.143-148
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• 2022

It is very important for metaverse, mobile robot, and user location services to analyze the images continuously taken using a mobile smartphone or robot's monocular camera to estimate the camera's location. So far, PnP-related techniques have been applied to calculate the position. In this paper, the camera's moving direction is obtained using the essential matrix in the epipolar geometry applied to successive images, and the camera's continuous moving position is calculated through geometrical equations. A new estimation method was proposed, and its accuracy was verified through simulation. This method is completely different from the existing method and has a feature that it can be applied even if there is only one or more matching feature points in two or more images.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.11-17
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• 2010

In this paper, we propose HMM-based upper-body gesture. First, to recognize gesture of space, division about pose that is composing gesture once should be put priority. In order to divide poses which using interface, we used two IR cameras established on front side and side. So we can divide and acquire in front side pose and side pose about one pose in each IR camera. We divided the acquired IR pose image using SVM's non-linear RBF kernel function. If we use RBF kernel, we can divide misclassification between non-linear classification poses. Like this, sequences of divided poses is recognized by gesture using HMM's state transition matrix. The recognized gesture can apply to existent application to do mapping to OS Value.

• Journal of Sensor Science and Technology
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• v.19 no.3
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• pp.246-257
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• 2010

Autonomous guided robot navigation which consists of following unknown paths and avoiding unknown obstacles has been a fundamental technique for unmanned robots in outdoor environments. The unknown path following requires techniques such as path recognition, path planning, and robot pose estimation. In this paper, we propose a novel sensor fusion system for autonomous guided robot navigation in outdoor environments. The proposed system consists of three monocular cameras and an array of nine infrared range sensors. The two cameras equipped on the robot's right and left sides are used to recognize unknown paths and estimate relative robot pose on these paths through bayesian sensor fusion method, and the other camera equipped at the front of the robot is used to recognize abrupt curves and unknown obstacles. The infrared range sensor array is used to improve the robustness of obstacle avoidance. The forward camera and the infrared range sensor array are fused through rule-based method for obstacle avoidance. Experiments in outdoor environments show the mobile robot with the proposed sensor fusion system performed successfully real-time autonomous guided navigation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.702-707
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• 2004

This article is concerned with container pose estimation using CCD a camera and a range sensor. In particular, the issues of characteristic point extraction and image noise reduction are described. The Euler-Lagrange equation for gaussian and random noise reduction is introduced. The alternating direction implicit(ADI) method for solving Euler-Lagrange equation based on partial differential equation(PDE) is applied. The vertex points as characteristic points of a container and a spreader are founded using k order curvature calculation algorithm since the golden and the bisection section algorithm can't solve the local minimum and maximum problems. The proposed algorithm in image preprocess is effective in image denoise. Furthermore, this proposed system using a camera and a range sensor is very low price since the previous system can be used without reconstruction.