Abstract: Provided are a walk-assistive robot and a method of controlling the same. The method of controlling the walk-assistive robot includes: obtaining ground information that is information regarding ground a walking direction; determining control patterns of the walk-assistive robot by analyzing the obtained ground information; and controlling the walk-assistive robot based on the determined control patterns.

Abstract: A control method of a walking assist robot, may include: estimating a wearer's location on a map including walking environment information; determining a walking environment in a direction in which the wearer moves; and selecting a control mode for assisting the wearer's walking according to the walking environment.

Abstract: Disclosed herein is a control method of a robot including: calculating hardness information about the ground on which a wearer moves; and controlling the robot according to the calculated hardness information.

Abstract: A mobile apparatus and a localization method thereof which perform localization of the mobile apparatus using a distributed filter system including a plurality of local filters independently operated and one fusion filter integrating results of localization performed through the respective local filters, and additionally apply accurate topological absolute position information to the distributed filter system to improve localization performance (accuracy, convergence and speed in localization, etc.) of the mobile apparatus on a wide space. The mobile apparatus includes at least one sensor, at least one first distribution filter generating current relative position information using a value detected by the at least one sensor, at least one second distribution filter generating current absolute position information using the value detected by the at least one sensor, and a fusion filter integrating the relative position information and the absolute position information to perform localization.

Abstract: A method of recognizing stairs in a 3D data image includes an image acquirer that acquires a 3D data image of a space in which stairs are located. An image processor calculates a riser height between two consecutive treads of the stairs in the 3D data image, identifies points located between the two consecutive treads according to the calculated riser height, and detects a riser located between the two consecutive treads through the points located between the two consecutive treads. Then, the image processor calculates a tread depth between two consecutive risers of the stairs in the 3D data image, identifies points located between the two consecutive risers according to the calculated tread depth, and detects a tread located between the two consecutive risers through the points located between the two consecutive risers.

Abstract: A robot and a method for creating a robot path. The method for planning the robot path includes generating a depth map including a plurality of cells by measuring a distance to an object, dividing a boundary among the plurality of cells into a plurality of partitions according to individual depth values of the cells, and extracting a single closed loop formed by the divided boundary, obtaining a position and shape of the object located at a first time through the extracted single closed loop, calculating a probability that the object is located at a second time after t seconds on the basis of the obtained position and shape of the object located at the first time, and creating a moving path simultaneously while avoiding the object according to the calculated probability, thereby creating an optimum path without colliding with the object.

Abstract: A surgical robot may include: an image information acquisition unit configured to acquire image information of an intra-abdominal environment while the surgical robot performs a surgical operation; and/or a controller configured to recognize positions of an endoscope and a tool, mounted on the surgical robot, based on the acquired image information and kinematic information of links included in the endoscope and the tool. A surgical robot may include: an image information acquisition unit configured to acquire image information of an intra-abdominal environment while the surgical robot performs a surgical operation; an inertia measurement unit configured to acquire inertia measurement information of the surgical robot; and/or a controller configured to recognize positions of an endoscope and a tool, mounted on the surgical robot, based on the acquired image information and the inertia measurement information.

Abstract: A moving robot and a method to build a map for the same, wherein a 3D map for an ambient environment of the moving robot may be built using a Time of Flight (TOF) camera that may acquire 3D distance information in real time. The method acquires 3D distance information of an object present in a path along which the moving robot moves, accumulates the acquired 3D distance information to construct a map of a specific level and stores the map in a database, and then hierarchically matches maps stored in the database to build a 3D map for a set space. This method may quickly and accurately build a 3D map for an ambient environment of the moving robot.

Abstract: A control method of a walking assist robot, may include: estimating a wearer's location on a map including walking environment information; determining a walking environment in a direction in which the wearer moves; and selecting a control mode for assisting the wearer's walking according to the walking environment.

Abstract: Disclosed herein is a control method of a robot including: calculating hardness information about the ground on which a wearer moves; and controlling the robot according to the calculated hardness information.

Abstract: A method of controlling a mobile apparatus includes acquiring a first original image and a second original image, extracting a first feature point of the first original image and a second feature point of the second original image, generating a first blurring image and a second blurring image by blurring the first original image and the second original image, respectively, calculating a similarity between at least two images of the first original image, the second original image, the first blurring image, and the second blurring image, determining a change in scale of the second original image based on the calculated similarity, and controlling at least one of an object recognition and a position recognition by matching the second feature point of the second original image to the first feature point of the first original image based on the change in scale.

Abstract: A simultaneous localization and map building method of a mobile robot including an omni-directional camera. The method includes acquiring an omni-directional image from the omni-directional camera, dividing the obtained omni-directional image into upper and lower images according to a preset reference to generate a first image, which is the lower image, and a second image, which is the upper image, extracting feature points from the first image and calculating visual odometry information calculating visual odometry information to track locations of the extracted feature points based on a location of the omni-directional camera, and performing localization and map building of the mobile robot using the calculated visual odometry information and the second image as an input of an extended Kalman filter.

Abstract: Provided are a walk-assistive robot and a method of controlling the same. The method of controlling the walk-assistive robot includes: obtaining ground information that is information regarding ground a walking direction; determining control patterns of the walk-assistive robot by analyzing the obtained ground information; and controlling the walk-assistive robot based on the determined control patterns.

Abstract: A method for estimating a location of a device uses a color image and a depth image. The method includes matching the color image to the depth image, generating a 3D reference image based on the matching, generating a 3D object image based on the matching, extracting a 2D reference feature point from the reference image, extracting a 2D reference feature point from the object image, matching the extracted reference feature point from the reference image to the extracted reference feature point from the object image, extracting a 3D feature point from the object image using the matched 2D reference feature point, and estimating the location of the device based on the extracted 3D feature point.

Abstract: Surgical trocars, and image acquisition method using the same, include a body having a passage configured to receive at least one surgical instrument, and at least one camera movably coupled to an outer wall of the body.

Abstract: A walking robot and a simultaneous localization and mapping method thereof in which odometry data acquired during movement of the walking robot are applied to image-based SLAM technology so as to improve accuracy and convergence of localization of the walking robot. The simultaneous localization and mapping method includes acquiring image data of a space about which the walking robot walks and rotational angle data of rotary joints relating to walking of the walking robot, calculating odometry data using kinematic data of respective links constituting the walking robot and the rotational angle data, and localizing the walking robot and mapping the space about which the walking robot walks using the image data and the odometry data.

Abstract: Disclosed herein are a feature point used to localize an image-based robot and build a map of the robot and a method of extracting and matching an image patch of a three-dimensional (3D) image, which is used as the feature point. It is possible to extract the image patch converted into the reference image using the position information of the robot and the 3D position information of the feature point. Also, it is possible to obtain the 3D surface information with the brightness values of the image patches to obtain the match value with the minimum error by a 3D surface matching method of matching the 3D surface information of the image patches converted into the reference image through the ICP algorithm.

Abstract: Disclosed herein are an endoscope system and a control method thereof. The control method includes acquiring plural omnidirectional images of the surroundings of an endoscope using a stereo omnidirectional camera mounted on the endoscope, calculating distances between the endoscope and an object around the endoscope using the acquired plural omnidirectional images, and executing an operation to avoid collision between the endoscope and the object around the endoscope based on the calculated distances, thus facilitating safe operation of the endoscope.

Abstract: A 3D object tracking method and apparatus in which a model of an object to be tracked is divided into a plurality of polygonal planes and the object is tracked using texture data of the respective planes and geometric data between the respective planes to enable more precise tracking. The 3D object tracking method includes modeling the object to be tracked to generate a plurality of planes, and tracking the plurality of planes, respectively. The modeling of the object includes selecting points from among the plurality of planes, respectively, and calculating projective invariants using the selected points.

Abstract: A system and method for extracting 3D coordinates, the method includes obtaining, by a stereoscopic image photographing unit, two images of a target object, and obtaining 3D coordinates of the object on the basis of coordinates of each pixel of the two images, measuring, by a Time of Flight (TOF) sensor unit, a value of a distance to the object, and obtaining 3D coordinates of the object on the basis of the measured distance value, mapping pixel coordinates of each image to the 3D coordinates obtained through the TOF sensor unit, and calibrating the mapped result, determining whether each set of pixel coordinates and the distance value to the object measured through the TOF sensor unit are present, calculating a disparity value on the basis of the distance value or the pixel coordinates, and calculating 3D coordinates of the object on the basis of the calculated disparity value.