Yonsei University

Hyundai Motor Company

Kia Motors Corporation

Lg Electronics Inc.

Beomseong Kim

Euntai Kim

MinkYun Yoo

Hoon Ki Lee

Hyungsun Jang

Baehoon Choi

Seongkeun Park

Hyunju Kim

Youngwon Kim

HyunJu Kim

YoungWon Kim

Hoyoung Lee

Jisu Kim

Dongki Noh

Hoon Shung Lee

Sangyun Lee

Yeonsoo Kim

Wonkeun Yang

Juhyeon Lee

JuYun Ro

Jhonghyun An

The present invention relates to an image processing apparatus and a mobile robot including the same. The image processing apparatus according to an embodiment of the present invention includes an image acquisition unit for obtaining an image and a processor for performing signal processing on the image from the image acquisition unit, and the processor is configured to group super pixels in the image on the basis of colors or luminances of the image, calculate representative values of the super pixels and perform segmentation on the basis of the representative values of the super pixels. Accordingly, image segmentation can be performed rapidly and accurately.

Image processing apparatus and mobile robot including same

A self-learning robot, according to one embodiment of the present invention, comprises: a data receiving unit for sensing video data or audio data relating to an object located within a predetermined range; a data recognition unit for matching data received from the data receiving unit and data included in a database in the self-learning robot; a result output unit for outputting a matching result from the data recognition unit; a recognition result verifying unit for determining the accuracy of the matching result; a server communication unit for transmitting data received from the data receiving unit to a server, when the accuracy of the matching result determined by the recognition result verifying unit is lower than a predetermined level; and an action command unit for causing the self-learning robot to perform a pre-set object response action, when the accuracy of the matching result determined by the recognition result verifying unit is at least the predetermined level.

Self-learning robot

A road boundary detection system includes: an optical scanner configured to emit a light to an object to acquire measurement data reflected from the object; and a processor configured to extract a plurality of straight lines in order of a priority on each of the plurality of straight lines when a higher priority is given to a straight line including more of the contact points, based on the measurement data, configured to calculate a score of each straight line by assigning a score to the contact point included in the extracted straight lines and by assigning a score to a non-contact point included in other straight line having the same angle as the contact point, and configured to select a road boundary according to the priority of the calculated score of each straight lines.

Road boundary detection system and method, and vehicle using the same

An apparatus and method for extracting a feature point to recognize an obstacle using a laser scanner are provided. The apparatus includes a laser scanner that is installed at a front of a traveling vehicle and is configured to obtain laser scanner data having a plurality of layers in real time. In addition, a controller is configured to separate the laser scanner data obtained by the laser scanner into a plurality of layers to extract measurement data present in each layer and determine feature points of the measurement data to classify a type of obstacle based on a plurality of stored feature points.

Apparatus and method for extracting feature point for recognizing obstacle using laser scanner

A system for writing an occupancy grid map of a sensor centered coordinate system using a laser scanner includes a data unit that includes scan data read by the laser scanner, a past measurement map, and data relating to a movement of the sensor, a mapping unit for stochastically combining a current measurement map written from the scan data with a predicted map written by using the past measurement map and the data relating to a movement of the sensor, and a static and dynamic object detection unit for determining whether an object in the occupancy grid map is a static or a dynamic object by using a mapping algorithm of the mapping unit.

System and method for writing occupancy grid map of sensor centered coordinate system using laser scanner

An apparatus and method for recognizing an obstacle using a laser scanner are provided. The apparatus includes a laser scanner that is installed at the front of a traveling vehicle and is configured to acquire laser scanner data configured of multiple layers in real time and an imaging device installed at the front of the moving object and is configured to acquire video data. A controller is configured to divide first laser scanner data item and second laser scanner data item acquired by the laser scanner into multiple layers to extract measurement data items each existing in the respective layers and is configured to compare the measurement data items in the same layer of the multiple layers to determine whether an obstacle is present ahead of the traveling vehicle. Additionally, the controller is configured to combine the laser scanner data reflecting whether an obstacle is present with the video data.

Apparatus and method for recognizing obstacle using laser scanner

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Seoul, South Korea

Beomseong Kim