Tomtom Global Content B.v.

Tomtom Location Technology Germany Gmbh

Michael Hofmann

Olaf Booij

Dimitri Schachmann

Tinosch Kolagheichi-Ganjineh

Sebastian Ihlefeld

Nicolau Leal Werneck

Sergej Mann

Philipp Holzschneider

Mohsen Ghafoorian

Cedric Nugteren

Philipp Holzchneider

Nora Baka

Tinosch Ganjineh

A method and system for determining a geographical location and orientation of a vehicle travelling through a road network is disclosed. The method comprises obtaining, from one or more cameras associated with the vehicle travelling through the road network, a sequence of images reflecting the environment of the road network on which the vehicle is travelling, wherein each of the images has an associated camera location at which the image was recorded. A local map representation representing an area of the road network on which the vehicle is travelling is then generated using at least some of the obtained images and the associated camera locations. The generated local map representation is compared with a section of a reference map, the reference map section covering the area of the road network on which the vehicle is travelling, and the geographical location and orientation of the vehicle within the road network is determined based on the comparison. Methods and systems for generating and/or updating an electronic map using data obtained by a vehicle travelling through a road network represented by the map are also disclosed.

Method and system for video-based positioning and mapping utilizing a pixel wise segmentation on an image of a detected object

A method is provided of training a generative adversarial network for performing semantic segmentation of images. The generative adversarial network includes a generator neural network and a discriminator neural network. The method includes providing an image as input to the generator neural network, receiving a predicted segmentation map for the image from the generator neural network, providing i) the image, ii) the predicted segmentation map, and iii) ground-truth label data corresponding to the image, as distinct training inputs to the discriminator neural network, determining a set of one or more outputs from the discriminator neural network in response to said training inputs, and training the generator neural network using a loss function that is a function of said set of outputs from the discriminator neural network.

Generative adversarial networks for image segmentation

Method and system for video-based positioning and mapping

A method and system for determining a geographical location and orientation of a vehicle () travelling through a road network is disclosed. The method comprises obtaining, from one or more cameras () associated with the vehicle () travelling through the road network, a sequence of images () reflecting the environment of the road network on which the vehicle () is travelling, wherein each of the images has an associated camera location at which the image was recorded. A local map representation representing an area of the road network on which the vehicle () is travelling is then generated using at least some of the obtained images and the associated camera locations. The generated local map representation is compared with a section of a reference map, the reference map section covering the area of the road network on which the vehicle () is travelling, and the geographical location and orientation of the vehicle () within the road network is determined based on the comparison. Methods and systems for generating and/or updating an electronic map using data obtained by a vehicle () travelling through a road network represented by the map are also disclosed.

Method and system for image-based positioning and mapping for a road network utilizing object detection

Growing community of inventors

Amsterdam, Netherlands

Michael Hofmann