• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.6
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• pp.10-21
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• 2000

The Cellular Noninear Networks structure for Distance Transform(DT) and the robust optical flow detection algorithm based on the DT are proposed. For some applications of optical flows such as target tracking and camera ego-motion computation, correct optical flows at a few feature points are more useful than unreliable one at every pixel point. The proposed algorithm is for detecting the optical flows on the trajectories only of the feature points. The translation lengths and the directions of feature movements are detected on the trajectories of feature points on which Distance Transform Field is developed. The robustness caused from the use of the Distance Transform and the easiness of hardware implementation with local analog circuits are the properties of the proposed structure. To verify the performance of the proposed structure and the algorithm, simulation has been done about various images under different noisy environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.49-52
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• 2019

Braille guide block of sidewalk is an essential facility for independent walking of the blind. The blind walks while checking the braile guide blocks with white cane and sense of sole. When they leave the braile area, they face difficulties until they find the braile guide blocks again. In this paper, we propose an algorithm that guides the walking of the blind by determining whether they follows the braille guide blocks safely. For this purpose, the slope of the braille block is selected as a feature and a 3-line detector is introduced. Also the slopes are stabilized using spatial filtering to deal with breaks or junctions of the braille block during the progress and temporal filtering to cope with ego-motion of the blind. Through simulations using a dataset obtained from the real sidewalks and indoors, it can be shown that the proposed algorithm can successfully estimate the walking direction and determine whether the blind is out of the braille guide block area.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.