NAVER LABS CORPORATION

A localization method includes scanning a surrounding space by using laser outputted from a reference region; processing spatial information about the surrounding space based on a reflection signal of the laser; extracting feature vectors to which the spatial information has been reflected by using a deep learning network which uses space vectors including the spatial information as input data; and comparing the feature vectors with preset reference map data, and thus estimating location information about the reference region.

The data collecting method includes: collecting first and second sensor data respectively through a first and a second sensors while a data collecting apparatus moves within a target area, and tagging a first and a second timestamp values respectively to the first and the second sensor data; generating map data of the target area and location data at a point of time corresponding to the first timestamp value, based on the first sensor data; generating map information of the target area based on the map data, and generating moving path information on the map based on the location data; and estimating a sensing location at a point of time corresponding to the second timestamp value based on the moving path information, and tagging the sensing location to the second sensor data.

The trajectory tracking method for a mobile electronic device may include tracking a trajectory of the electronic device by using results of pose estimation using odometry and results of pose estimation using visual localization (VL) as camera pose information.

A localization method includes scanning a surrounding space by using laser outputted from a reference region; processing spatial information about the surrounding space based on a reflection signal of the laser; extracting feature vectors to which the spatial information has been reflected by using a deep learning network which uses space vectors including the spatial information as input data; and comparing the feature vectors with preset reference map data, and thus estimating location information about the reference region.

A position recognition method and a system based on visual information processing are disclosed A position recognition method according to one embodiment including the steps of: generating a frame image through a camera; transmitting, to a server, a first global pose of the camera and the generated frame image; and receiving, from the server, a second global pose of the camera estimated on the basis of a pose of an object included in the transmitted frame image.

A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data including signal strength values and location labels; and a subset of unlabeled data including signal strength values and not including labels indicative of locations; training a variational autoencoder to minimize a reconstruction loss of the signal strength values of the training data, where the variational autoencoder includes encoder neural networks and decoder neural networks; and training a classification neural network to minimize a prediction loss on the labeled data, where the classification neural network generates a predicted location based on the latent variable, and where the encoder neural networks and the classification neural network form the predictor.

The trajectory tracking method for a mobile electronic device may include tracking a trajectory of the electronic device by using results of pose estimation using odometry and results of pose estimation using visual localization (VL) as camera pose information.

A method of identifying a current location, performed by a server having wireless communication with a terminal, includes receiving from the terminal, surrounding environment information input in natural language by a user; performing natural language processing on the surrounding environment information to extract place information about a reference place input by the user and positional relationship information about a positional relationship between the reference place and the user; searching a map database for the reference place corresponding to the place information; estimating a candidate region in which the user is likely to be located, based on the searched-for reference place and the positional relationship information; and determining the candidate region as the user's current location. Because the current location is identified based on the surrounding environment information input by the user, the user's current location can be identified more accurately.

A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data including signal strength values and location labels; and a subset of unlabeled data including signal strength values and not including labels indicative of locations; training a variational autoencoder to minimize a reconstruction loss of the signal strength values of the training data, where the variational autoencoder includes encoder neural networks and decoder neural networks; and training a classification neural network to minimize a prediction loss on the labeled data, where the classification neural network generates a predicted location based on the latent variable, and where the encoder neural networks and the classification neural network form the predictor.