Samsung Electronics Co., Ltd.

Kyungboo Jung

Wonhee Lee

Hyun Sung Chang

Young Hun Sung

Keechang Lee

Hyun Cheol Jeon

Minjung Son

Nahyup Kang

Hwiryong Jung

Chul Woo Kang

Sunghoon Hong

Hojin Ju

Inwoo Ha

Chul Kyu Kang

Donghoon Sagong

Won-Hee Lee

Seungeon Kim

Hyewon Moon

Chui Kang

Hwi-Ryong Jung

Chu Woo Kang

A processor-implemented method with pose estimation includes: determining depth data by sensing a depth of a target scene comprising planes orthogonal to each other; determining normal vectors corresponding to depth points of the depth data; determining orientation data of a device by comparing the normal vectors to orientation candidates; and determining position data of the device based on distances from the device to the planes.

Method and apparatus with pose estimation

An operation method of a navigation apparatus includes: obtaining valid global positioning system (GPS) data at a current time point corresponding to a current position of a target device; determining first neighboring map elements corresponding to a first region indicated by the valid GPS data at the current time point from among a plurality of map elements of map data; and determining a pose parameter of the target device at the current time point based on a first direction specified by at least a portion of the first neighboring map elements.

Navigation apparatus and operation method of navigation apparatus

An electronic device includes: a timekeeping processor configured to: determine a sensing time point of reference sensing data, generated through sensing by a reference sensor of a plurality of sensors, and a sensing time point of other sensing data of another sensor of the plurality of sensors, based on a clock rate of the timekeeping processor; determine a time difference between the sensing time point of the reference sensing data and the sensing time point of the other sensing data; and determine a task latency from the sensing time point of the reference sensing data to a task complete time point based on the clock rate of the timekeeping processor; and one or more other processors configured to correct, based on the task latency, a task result processed according to localization of the electronic device determined for the sensing time point of the reference sensing data based on the determined time difference, the reference sensing data, and the other sensing data.

Electronic device and method with independent time point management

A processor-implemented motion information estimating method includes: estimating motion information based on at least one set of initial motion information of a vehicle that is obtained from at least one sensor; predicting a plurality of sets of image feature information corresponding to a periphery of the vehicle based on the estimated motion information; obtaining a plurality of sets of detected image feature information detected from an input image obtained from an image sensor and an accuracy of each of the sets of the detected image feature information using a neural network; evaluating a reliability of each of the sets of the detected image feature information by comparing the sets of the predicted image feature information and the sets of the detected image feature information; and correcting the estimated motion information based on at least one set of the sets of the detected image feature information selected based on a result of the evaluating of the reliability and on the accuracy.

Method and apparatus with motion information estimation

Disclosed is a method and apparatus with localization. The method includes determining a sensor-based reference position using a position sensor of the vehicle, determining an image-based reference position of the vehicle based on image information of the vehicle captured using a camera of the vehicle, setting an acceptance range for the image-based reference position based on a driving situation of the vehicle, and comparing an error level of the image-based reference position to the acceptance range and estimating a current position of the vehicle.

Method and apparatus with localization

A stereo matching method includes obtaining a first feature map associated with a first view image and a second feature map associated with a second view image using a neural network model-based feature extractor, determining respective matching costs between a reference pixel of the first view image and candidate pixels of the second view image using the first feature map and the second feature map, and determining a pixel corresponding to the reference pixel among the candidate pixels based on the determined matching costs.

Stereo matching method and apparatus, image processing apparatus, and training method therefor

A method for generating a three-dimensional (3D) lane model, the method including calculating a free space indicating a driving-allowed area based on a driving image captured from a vehicle camera, generating a dominant plane indicating plane information of a road based on either or both of depth information of the free space and a depth map corresponding to a front of the vehicle, and generating a 3D short-distance road model based on the dominant plane.

Method and apparatus for generating three-dimensional (3D) road model

An apparatus and method to train an image recognition model to accurately estimate a location of a reference point for each class of landmark is disclosed. The apparatus and method use the image recognition model, which is trained based on calculating a class loss and a class-dependent localization loss from training data based on an image recognition model and training the image recognition model using a total loss comprising the class loss and the localization loss.

Method and apparatus to train image recognition model, and image recognition method and apparatus

A position information estimating method and apparatus are provided, wherein the position information estimating method includes estimating initial position information of a vehicle based on motion information of the vehicle that is obtained from one or more sensors, calculating search position information of one or more objects included in an image of surroundings of the vehicle based on the initial position information, evaluating a final reliability of each of the one or more objects based on the initial position information and the search position information, obtaining a filter adaptation parameter corresponding to a weight of the search position information based on the final reliability, and estimating final position information of the vehicle based on the initial position information, the search position information, and the filter adaptation parameter.

Method and apparatus for estimating position

A processor-implemented method includes: estimating, from frame images of consecutive frames acquired from one or more sensors, short-term ego-motion information of the one or more sensors; estimating long-term ego-motion information of the one or more sensors from the frame images; determining attention information from the short-term ego-motion information and the long-term ego-motion information; and determining final long-term ego-motion information of a current frame, of the consecutive frames, based on the long-term ego-motion information and the attention information.

Method and apparatus with ego motion information estimation

A method and apparatus for displaying a virtual route estimates a position of a vehicle based on sensing data received asynchronously from sensors, and outputs a three-dimensional (3D) virtual route generated by registering a driving environment model corresponding to a driving environment of the vehicle and the position of the vehicle in map information.

Method and apparatus for displaying virtual route

Localization apparatuses and methods are disclosed where a localization apparatus extracts a feature of an object from an input image, generates an image in which the object is projected with respect to localization information of a device based on map data, and evaluates the localization information based on feature values corresponding to vertices included in a projection image.

Method and apparatus for localization based on images and map data

A vehicle localization method and a vehicle localization apparatus are disclosed. The vehicle localization method includes estimating an initial position of a vehicle based on data sensed by a position sensor, determining a driving lane of the vehicle based on a front-view image captured from the vehicle, correcting the initial position of the vehicle based on the driving lane, and determining a position of the vehicle in the driving lane based on geometry information of a lane boundary in the front-view image.

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Seoul, South Korea

Kyungboo Jung