• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.3 s.309
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• pp.8-15
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• 2006

Obstacle detection is an important task for many mobile robot applications. The methods using stereo vision and optical flow are computationally expensive. Therefore, this paper presents a vision-based obstacle detection method using only two view images. The method uses a single passive camera and odometry, performs in real-time. The proposed method is an obstacle detection method using 3D reconstruction from taro views. Processing begins with feature extraction for each input image using Dr. Lowe's SIFT(Scale Invariant Feature Transform) and establish the correspondence of features across input images. Using extrinsic camera rotation and translation matrix which is provided by odometry, we could calculate the 3D position of these corresponding points by triangulation. The results of triangulation are partial 3D reconstruction for obstacles. The proposed method has been tested successfully on an indoor mobile robot and is able to detect obstacles at 75msec.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.29-32
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• 2002

This paper suggest target detection algorithm for mobile robot control using color and shape recognition. In many cases, ultrasonic sensor(USS) is used in mobile robot system to measure the distance between obstacles. But with only USS, it may have many restrictions. So we attached CCD camera to mobile robot to overcome its restrictions. If visual information is given to robot system then robot system will be able to accomplish more complex mission successfully. With acquired vision data, robot looks for target by color and recognize its shape.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.427-433
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• 2022

Obstacle detection, collision recognition, and avoidance technologies are required the collision avoidance technology for UAVs. In this paper, considering collinear multiple static obstacle, we propose an obstacle detection algorithm using LiDAR and a collision recognition and avoidance algorithm based on CPA. Preprocessing is performed to remove the ground from the LiDAR measurement data before obstacle detection. And we detect and classify obstacles in the preprocessed data using the K-means clustering algorithm. Also, we estimate the absolute positions of detected obstacles using relative navigation and correct the estimated positions using a low-pass filter. For collision avoidance with the detected multiple static obstacle, we use a collision recognition and avoidance algorithm based on CPA. Information of obstacles to be avoided is updated using distance between each obstacle, and collision recognition and avoidance are performed through the updated obstacles information. Finally, through obstacle location estimation, collision recognition, and collision avoidance result analysis in the Gazebo simulation environment, we verified that collision avoidance is performed successfully.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.129-135
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• 2016

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which "global" refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.3
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• pp.143-150
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• 2008

In this paper, we investigate a real-time environment recognition system based on stereo vision for moving object. This system consists of stereo matching, obstacle detection and distance estimation. In stereo matching part, depth maps can be obtained real road images captured adjustable baseline stereo vision system using belief propagation(BP) algorithm. In detection part, various obstacles are detected using only depth map in case of both v-disparity and column detection method under the real road environment. Finally in estimation part, asymmetric parabola fitting with NCC method improves estimation of obstacle detection. This stereo vision system can be applied to many applications such as unmanned vehicle and robot.

• Current Optics and Photonics
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• v.6 no.5
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• pp.463-472
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• 2022

At certain wavelengths, single-pixel imaging is considered to be a solution that can achieve high quality imaging and also reduce costs. However, achieving imaging of complex scenes is an overhead-intensive process for single-pixel imaging systems, so low efficiency and high consumption are the biggest obstacles to their practical application. Improving efficiency to reduce overhead is the solution to this problem. Salient object detection is usually used as a pre-processing step in computer vision tasks, mimicking human functions in complex natural scenes, to reduce overhead and improve efficiency by focusing on regions with a large amount of information. Therefore, in this paper, we explore the implementation of salient object detection based on single-pixel imaging after a single pixel, and propose a scheme to reconstruct images based on Fourier bases and use U²Net models for salient object detection.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.549-559
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• 2015

In this paper, an efficient dynamic reactive motion planning method for an autonomous vehicle in a dynamic environment is proposed. The purpose of the proposed method is to improve the robustness of autonomous robot motion planning capabilities within dynamic, uncertain environments by integrating a virtual plane-based reactive motion planning technique with a sensor fusion-based obstacle detection approach. The dynamic reactive motion planning method assumes a local observer in the virtual plane, which allows the effective transformation of complex dynamic planning problems into simple stationary ones proving the speed and orientation information between the robot and obstacles. In addition, the sensor fusion-based obstacle detection technique allows the pose estimation of moving obstacles using a Kinect sensor and sonar sensors, thus improving the accuracy and robustness of the reactive motion planning approach. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles in hostile dynamic environments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.7
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• pp.611-619
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• 2016

This paper proposes a collision prevention system for an agricultural unmanned helicopter. The collision prevention system consists of an obstacle detection system, a mapping algorithm, and a collision avoidance algorithm. The obstacle detection system based on a LiDAR sensor is implemented in the unmanned helicopter and acquires distance information of obstacles in real-time. Then, an obstacle mapping is carried out by combining the distance to the obstacles with attitude/location data of the unmanned helicopter. In order to prevent a collision, alert is activated to an operator based on the map when the vehicle approaches to the obstacles. Moreover, the developed collision prevention system is verified through flight test simulating a flight pattern aerial spraying.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.623-629
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• 2018

In this paper, we propose a real-time obstacle detection and avoidance path generation algorithm for UAV. 2-D Lidar is used to detect obstacles, and the detected obstacle data is used to generate real-time histogram for local avoidance path and a 2-D SLAM map used for global avoidance path generation to the target point. The VFH algorithm for local avoidance path generation generates a real-time histogram of how much the obstacles are distributed in the vector direction and distance, and this histogram is used to generate the local avoidance path when detecting near fixed or dynamic obstacles. We propose an algorithm, called modified $RRT^*-Smart$, to overcome existing limitations. That generates global avoidance path to the target point by creating lower costs because nodes are checked whether or not straight path to a target point, and given arbitrary lengths and directionality to the target points when nodes are created. In this paper, we prove the efficient avoidance maneuvering through various simulation experiment environment by creating efficient avoidance paths.