• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.625-627
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• 2005

Existent distance measurement that use camera is method that use both Stereo Camera and Monocular Camera, There is shortcoming that method that use Stereo Camera is sensitive in effect of a lot of expenses and environment variables, and method that use Monocular Camera are big computational complexity and error. In this study, reduce expense and error using Monocular Camera and I suggest algorithm that measure distance, Extract features using scale Invariant features Transform(SIFT) for distance measurement, and this measures distance through features matching and geometrical analysis, Proposed method proves measuring distance with wall by geometrical analysis free wall through feature point abstraction and matching.

• Journal of Auto-vehicle Safety Association
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• v.12 no.3
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• pp.43-51
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• 2020

Currently, the second level of the six stages of self-driving technology, as defined by SAE, is commercialized, and the third level is preparing for commercialization. The purpose of ACC is to be evaluated as a system useful for preventing and preventing accidents by minimizing driver fatigue through longitudinal speed control and relative distance control of the vehicle. In this regard, for the study of safety assessment methods in the practical environment of ACC. Distance measurement method using monocular camera and data acquisition equipment such as DGPS are utilized. Based on the evaluation scenario considering the domestic road environment proposed by the preceding study, the relative distance obtained from equipment such as DPGS and the relative distance using a monocular camera in the actual test is verified by comparing and analyzing the safety assessment. The comparison by scenario results showed a minimum error rate of 3.83% in Scenario 1 and a maximum of 14.61% in Scenario 6. The cause of the maximum error is that the lane recognition is not accurate in the camera image and irregular operation conditions such as rushing in or exiting the surrounding area from the walkway. It is expected that safety evaluation using a monocular camera will be possible for other ADAS systems in the future.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.367-371
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• 2015

In this paper, a novel localization method for a monocular camera is proposed by using a feature-based probabilistic map. The localization of a camera is generally estimated from 3D-to-2D correspondences between a 3D map and an image plane through the PnP algorithm. In the computer vision communities, an accurate 3D map is generated by optimization using a large number of image dataset for camera pose estimation. In robotics communities, a camera pose is estimated by probabilistic approaches with lack of feature. Thus, it needs an extra system because the camera system cannot estimate a full state of the robot pose. Therefore, we propose an accurate localization method for a monocular camera using a probabilistic approach in the case of an insufficient image dataset without any extra system. In our system, features from a probabilistic map are projected into an image plane using linear approximation. By minimizing Mahalanobis distance between the projected features from the probabilistic map and extracted features from a query image, the accurate pose of the monocular camera is estimated from an initial pose obtained by the PnP algorithm. The proposed algorithm is demonstrated through simulations in a 3D space.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.1-11
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• 2013

The determination of the pose of the imaging camera is a fundamental problem in computer vision. In the monocular case, difficulties in determining the scene scale and the limitation to bearing-only measurements increase the difficulty in estimating camera pose accurately. Many mobile phones now contain inertial measurement devices, which may lend some aid to the task of determining camera pose. In this study, by means of simulation and real-world experimentation, we explore an approach to monocular camera localization that incorporates both observations of the environment and measurements from accelerometers and gyroscopes. The unscented Kalman filter was implemented for this task. Our main contribution is a novel approach to landmark initialization in a Kalman filter; we characterize the tolerance to noise that this approach allows.

• Journal of Auto-vehicle Safety Association
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• v.12 no.3
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• pp.34-42
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• 2020

ADAS (Advanced Driver Assistance Systems) uses sensors such as camera, radar, lidar and GPS (Global Positioning System). Among these sensors, the camera has many advantages compared with other sensors. The reason is that it is cheap, easy to use and can identify objects. In this paper, therefore, a theoretical formula was proposed to obtain the distance from the vehicle's front wheel to the lane using a monocular camera. And the validity of the theoretical formula was verified through the actual vehicle test. The results of the actual vehicle test in scenario 4 resulted in a maximum error of 0.21 m. The reason is that it is difficult to detect the lane in the curved road, and it is judged that errors occurred due to the occurrence of significant yaw rates. The maximum error occurred in curve road condition, but the error decreased after lane return. Therefore, the proposed theoretical formula makes it possible to assess the safety of the LKA system.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.536-538
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• 2006

The most important factor of mobile robot is to build a map for surrounding environment and estimate its localization. This paper proposes a real-time localization and map building method through 3-D reconstruction using scale invariant feature from monocular camera. Mobile robot attached monocular camera looking wall extracts scale invariant features in each image using SIFT(Scale Invariant Feature Transform) as it follows wall. Matching is carried out by the extracted features and matching feature map that is transformed into absolute coordinates using 3-D reconstruction of point and geometrical analysis of surrounding environment build, and store it map database. After finished feature map building, the robot finds some points matched with previous feature map and find its pose by affine parameter in real time. Position error of the proposed method was maximum. 8cm and angle error was within $10^{\circ}$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.182-188
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• 2019

Autonomous mobile robots need SLAM (simultaneous localization and mapping) to look for the location and simultaneously to make the map around the location. In order to achieve visual SLAM, it is necessary to form an algorithm that detects and extracts feature points from camera images, and gets the camera pose and 3D points of the features. In this paper, we propose MPROSAC algorithm which combines MSAC and PROSAC, and compare the performance of optimization method and the filtering method for feature-based monocular visual SLAM. Sparse Bundle Adjustment (SBA) is used for the optimization method and the extended Kalman filter is used for the filtering method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.606-609
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• 2001

This paper's purpose is to obtain 3-Dimension information by using a monocular camera. This system embodies to obtain the height of object by using trigonometry method between a reference point of circumstance and an object. It is possible to build up system regardless of operating system, and then set it up. An comfortable USB port camera is used everywhere without the capture board. The internet can be used by using the applet and JMF everywhere. We regard the camera as a fixed. And we have developed a Real-Time JPEG/RTP Network Camera system using UDP/IP on Ethernet.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2326-2328
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• 2001

This paper's purpose is to obtain 3-Dimension information by using a monocular camera. This system embodies to obtain the height of object by using trigonometry method between a reference point of circumstance and an object. It is possible to build up system regardless of operating system, and then set it up. An comfortable USB port camera is used everywhere without the capture board. The internet can be used by using the applet and JMF everywhere. We regard the camera as a fixed. And we have developed a Real-Time JPEG/RTP Network Camera system using UDP/IP on Ethernet.