• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.12
• /
• pp.1046-1052
• /
• 2016

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

• Journal of Sensor Science and Technology
• /
• v.29 no.4
• /
• pp.237-242
• /
• 2020

This paper deals with relative position estimation using a Kalman filter (KF) based on inertial sensors that have been widely used in various biomechanics-related outdoor applications. In previous studies, the relative position is determined using relative orientation and predetermined segment-to-joint (S2J) vectors, which are assumed to be constant. However, because body segments are influenced by soft tissue artifacts (STAs), including the deformation and sliding of the skin over the underlying bone structures, they are not constant, resulting in significant errors during relative position estimation. In this study, relative position estimation was performed using a KF, where the S2J vectors were adopted as time-varying states. The joint constraint and the variations of the S2J vectors were used to develop a measurement model of the proposed KF. Accordingly, the covariance matrix corresponding to the variations of the S2J vectors continuously changed within the ranges of the STA-causing flexion angles. The experimental results of the knee flexion tests showed that the proposed KF decreased the estimation errors in the longitudinal and lateral directions by 8.86 and 17.89 mm, respectively, compared with a conventional approach based on the application of constant S2J vectors.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.9
• /
• pp.1416-1423
• /
• 2017

Lane detection technology is one of the most important issues on car safety and self-driving capability of autonomous vehicle. This paper introduces an accurate lane detection scheme based on OCC(Optical Camera Communication) for moving vehicles. For lane detection of moving vehicles, the streetlights and the front camera of the vehicle were used for a transmitter and a receiver, respectively. Based on the angle information of multiple streetlights in a captured image, the distance from sidewalk can be calculated using non-linear regression analysis. Simulation results show that the proposed scheme shows robust performance of accurate lane detection.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.2
• /
• pp.299-306
• /
• 2016

In this paper, a method of shape prediction of an endoscope handling robot that can imitate a surgeon's behavior using a sensor network is suggested. Unit sensors, which are composed of a 3-axis magnetometer and 3-axis accelerometer pair comprise the network through CAN bus communication. Each unit of the sensor is used to detect the angle of the points in the longitudinal direction of the robot, which is made from a flexible tube. The signals received from the sensor network were filtered using a low pass Butterworth filter. Here, a Butterworth filter was designed for noise removal. Finally, the Euler angles were extracted from the signals, in which the noise was filtered by the low path Butterworth filter. Using this Euler angle, the position of each sensor on the sensor network is estimated. The robot body was assumed to consist of links and joints. The position of each sensor can be assumed to be attached to the center of each link. The position of each link was determined using the Euler angle and kinematics equation. The interpolation was carried out between the positions of the sensors to be able to connect each point smoothly and obtain the final posture of the endoscope in operation. The experimental results showed that the shape of the colonoscope can be visualized using the Euler angles evaluated from the sensor network suggested and the shape of serial link estimated from the kinematics chain model.