Tomtom Global Content B.v.

Fugro N.v.

Fnv IP B.v.

Tomtom Location Technology Germany Gmbh

Olaf Booij

Evert Schippers

Fatemeh Karimi Nejadasl

Hendrik Wouters

Michael Hofmann

Sergej Mann

Nicolau Leal Werneck

Sebastian Ihlefeld

Tinosch Kolagheichi-Ganjineh

Dimitri Schachmann

Philipp Holzschneider

Pawel Michalak

Mohsen Ghafoorian

Cedric Nugteren

Philipp Holzchneider

Diego Dias Bispo Carvalho

Laurens Samson

Nora Baka

Moshen Ghafoorian

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 of training a generative adversarial network for performing semantic segmentation of images. The generative adversarial network includes a generator neural network and a critic neural network. The method includes using the generator neural network to generate predicted image segmentation maps from input images, wherein each predicted image segmentation map includes a classification prediction for each of a plurality of pixels of a respective input image, providing predicted image segmentation maps generated by the generator neural network to the critic neural network, training the critic neural network to determine weights for respective pixels of a predicted image segmentation map generated by the generator neural network, wherein the weight for each pixel is used to weight a pixel-wise cross entropy term in an objective function for the critic neural network, and using the weights determined by the critic neural network to train the generator neural network.

Training a generative adversarial network for performing semantic segmentation of images

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

A system for measuring the attitude of an object in a fixed frame of reference from a moving frame of reference, comprising a first imaging device for producing image data for the object from a first direction, a second imaging device for producing image data for the object from a second direction having a component perpendicular to the first direction and an attitude sensing arrangement for determining an attitude of the first and second imaging devices with respect to the fixed frame of reference at the time the respective images are taken and for generating attitude data. An image processing system analyses and combines the respective image data and attitude data to determine the attitude of the object. The system is particularly useful for determining attitude of offshore piles during piling operations or for monitoring the departure angles of pipes and cables during laying thereof.

Offshore positioning system and method

An image acquisition unit for obtaining data to generate at least one three-dimensional representation of at least one underwater structure is disclosed. The image acquisition unit includes a unit body, a plurality of cameras, a first laser light device, and a second laser light device. The first laser light device can operate based on a first illumination setting. The second laser light device can operate based a second illumination setting. The first and second cameras can be configured to capture light during the first illumination setting and generate a first set of data representative of the first laser projecting on the at least one underwater structure at a predetermined scan rate. The third and fourth cameras can be configured to capture light during the second illumination setting and generate a second set of data representative of the second laser projecting on the at least one underwater structure at the predetermined scan rate.

Underwater laser based modeling device

Growing community of inventors

Leiden, Netherlands

Olaf Booij