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 method for training a neural network for determining a task output with respect to a given task. The method includes: providing unlabeled and/or labelled encoder training records of measurement data; training the encoder network to map encoder training records to representations towards the goal that these representations, and/or or one or more work products derived from the representations, fulfil a self-consistency condition or correspond to ground truth; providing task training records that are labelled with ground truth; and training the association network and the task head networks towards the goal that, when a task training record is mapped to a representation using the encoder network, and the representation is mapped to a task output by the combination of the association network and the task head networks, the so-obtained task output corresponds to the ground truth with which the training record is labelled, as measured by a task loss function.

Abstract: A method for operating at least one trained classifier for measurement data. The classifier comprises a neural network with at least one feature extraction section and at least one classification section. The method includes: processing a record of measurement data with at least the feature extraction section of the classifier; determining a set of neurons in the feature extraction section that are activated by said processing; determining, from a given correspondence between activated neurons and attributes, a set of attributes whose presence in a scene captured by the measurement data is indicated by the activated neurons; comparing attributes to which classes are linked by a given knowledge graph with said determined set of attributes; and evaluating, from the result of this comparison, at least one estimated class as a class to which the scene captured by the record of measurement data is likely to belong.

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: A method for fusing state data via a control unit. State data of a first mobile unit and of an object ascertained via a sensor system of the first mobile unit are received. State data of an object ascertained via a sensor system of a second mobile unit and/or state data of the second mobile unit, transmitted via a communication link from the second mobile unit to the first mobile unit, are received. A node is created in a time-position diagram for each set of received state data of the first mobile unit, the second mobile unit, and the objects. A data optimization of the state data ascertained by the first mobile unit and/or by the second mobile unit is carried out. An optimization problem is created based on the optimized state data ascertained by the first mobile unit and the optimized state data received from the second mobile unit.

Abstract: An image classifier for classifying an input image x with respect to combinations of an object value o and an attribute value. The image classifier includes an encoder network that is configured to map the input image to a representation comprising multiple independent components; an object classification head network configured to map representation components of the input image to one or more object values; an attribute classification head network configured to map representation components of the input image to one or more attribute values; and an association unit configured to provide, to each classification head network, a linear combination of those representation components of the input image x that are relevant for the classification task of the respective classification head network. A method for training the image classifier is also provided.

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 system and method are provided for classifying objects in spatial data using a machine learned model, as well as a system and method for training the machine learned model. The machine learned model may comprise a content sensitive classifier, a location sensitive classifier and at least one outlier detector. Both classifiers may jointly distinguish between objects in spatial data being in-distribution or marginal-out-of-distribution. The outlier detection part may be trained on inlier examples from the training data, while the presence of actual outliers in the input data of the machine learnable model may be mimicked in the feature space of the machine learnable model during training. The combination of these parts may provide a more robust classification of objects in spatial data with respect to outliers, without having to increase the size of the training data.

Abstract: A method for fusing state data via a control unit. State data of a first mobile unit and of an object ascertained via a sensor system of the first mobile unit are received. State data of an object ascertained via a sensor system of a second mobile unit and/or state data of the second mobile unit, transmitted via a communication link from the second mobile unit to the first mobile unit, are received. A node is created in a time-position diagram for each set of received state data of the first mobile unit, the second mobile unit, and the objects. A data optimization of the state data ascertained by the first mobile unit and/or by the second mobile unit is carried out. An optimization problem is created based on the optimized state data ascertained by the first mobile unit and the optimized state data received from the second mobile unit.

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.