Abstract: A method for providing data for creating a digital map. The method includes: detecting surroundings sensor data of the surroundings during a measuring run of a physical system, preferably a vehicle, the surroundings sensor data capturing the surroundings in an at least partially overlapping manner, first surroundings sensor data including three-dimensional information, and second surroundings sensor data including two-dimensional information; extracting, with the aid of a first neural network situated in the physical system, at least one defined object from the first and second surroundings sensor data into first extracted data; and extracting, with the aid of a second neural network situated in the physical system, characteristic features including descriptors from the first extracted data into second extracted data, the descriptors being provided for a defined alignment of the second extracted data in a map creation process.

Abstract: A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.

Abstract: A computer-implemented method for supplying radar data. The method includes the following steps: receiving input data, the input data including satellite images; generating radar data using a trained machine learning algorithm, which is applied to the input data; and outputting the generated radar data.

Abstract: A method for training an artificial neural network uses training data that include first image data of a first image and second image data of a second image of an infrastructure. The first image includes a first feature, and the second image includes a second feature corresponding to the first image. The training data include a relative desired translation and a relative desired rotation between the first feature and the second feature. The training includes extracting the first feature from the first image and extracting the second feature from the second image using the artificial neural network. The extracted first feature is represented by first feature data having a first volume of data. The extracted second feature is represented by second feature data having a second volume of data. The training further includes ascertaining a relative translation and a relative rotation between the extracted first feature and the extracted second.

Abstract: Examples disclosed herein involve a computing system configured to (i) based on image data captured by a vehicle in an environment, obtain observations of a time-sequence of positions of an agent identified within the image data, (ii) generate a first updated time-sequence of positions of the agent by performing a first optimization operation that includes processing the observed time-sequence of positions by beginning with a position associated with an observation having the highest confidence in the time-sequence of observations and proceeding in a first direction, (iii) after generating the first updated time-sequence of positions, generate a second updated time-sequence of positions of the agent by performing a second optimization operation that includes processing the first updated time-sequence of positions in a second direction opposite the first direction, and (iv) derive a trajectory for the agent in the environment based on the second updated time-sequence of positions for the agent.

Abstract: A method for validating an up-to-dateness of a digital map by a control unit, the at least one digital map being received or retrieved. Measured data on the surroundings of the vehicle are received. A vehicle position within the at least one digital map is ascertained based on landmarks. Data of a driving assistance function are received. A comparison is carried out between the vehicle position within the at least one digital map and the data of the driving assistance function for validating the up-to-dateness of the at least one digital map. A transfer system, a control unit, a computer program as well as a machine-readable memory medium are also described.

Abstract: A method for training a neural convolutional network for determining, with the aid of the neural convolutional network, a localization pose of a mobile platform using a ground image. Using a first multitude of aerial image training cycles, each aerial image training cycle includes: providing a reference pose of the mobile platform; and providing an aerial image of the environment of the mobile platform in the reference pose; using the aerial image as an input signal of the neural convolutional network; determining the respective localization pose with the aid of an output signal of the neural convolutional network; and adapting the neural convolutional network to minimize a deviation of the respective localization pose determined using the respective aerial image from the respective reference pose.

Abstract: A method for providing data for creating a digital map. The method includes: detecting surroundings sensor data of the surroundings during a measuring run of a physical system, preferably a vehicle, the surroundings sensor data capturing the surroundings in an at least partially overlapping manner, first surroundings sensor data including three-dimensional information, and second surroundings sensor data including two-dimensional information; extracting, with the aid of a first neural network situated in the physical system, at least one defined object from the first and second surroundings sensor data into first extracted data; and extracting, with the aid of a second neural network situated in the physical system, characteristic features including descriptors from the first extracted data into second extracted data, the descriptors being provided for a defined alignment of the second extracted data in a map creation process.

Abstract: A computer-implemented method for supplying radar data. The method includes the following steps: receiving input data, the input data including satellite images; generating radar data using a trained machine learning algorithm, which is applied to the input data; and outputting the generated radar data.

Abstract: A method for training a neural convolutional network for determining, with the aid of the neural convolutional network, a localization pose of a mobile platform using a ground image. Using a first multitude of aerial image training cycles, each aerial image training cycle includes: providing a reference pose of the mobile platform; and providing an aerial image of the environment of the mobile platform in the reference pose; using the aerial image as an input signal of the neural convolutional network; determining the respective localization pose with the aid of an output signal of the neural convolutional network; and adapting the neural convolutional network to minimize a deviation of the respective localization pose determined using the respective aerial image from the respective reference pose.

Abstract: A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.