Abstract: A method for training a student network of a teacher-student training architecture for point-cloud registration for mapping an environment for navigation of a technical system. The teacher-student training architecture includes a teacher network and the student network.

Abstract: A method for matching environmental sensor scans that are designed to scan an environment and provide scans of the environment. The method includes the following method step. At least one matching transformation between a scan of an environmental sensor and a reference scan of an environmental sensor serving as a reference sensor is ascertained. When ascertaining the transformation, at least one further scan of the environmental sensor and/or of a further environmental sensor is taken into account.

Abstract: A computer-implemented method for generating a training data set for training a map generation module. The method includes: ascertaining a position value of the surroundings sensor data, wherein the position value of the surroundings sensor data is defined by a pose of the mobile unit; ascertaining a deviating position value of the surroundings sensor data, wherein the deviating position value deviates from the position value of the surroundings sensor data by a deviation value; ascertaining a map section of the electronic map based on the deviating position value, wherein the map section is disposed around the deviating position value of the surroundings sensor data; grouping the surroundings sensor data and the ascertained map section into a data set unit; and integrating the data set unit into the training data set. A method for training a map generation module is also described.

Abstract: A method for aligning scans of environmental sensors. In the method, scans of a first and a second environmental sensor are used. For each environmental sensor, a first scan recorded at a first point in time and a second scan of the same environment recorded at a second point in time following the first point in time are used. The first scan of the first environmental sensor and the first scan of the second environmental sensor are combined to form a first fused scan. The second scan of the first environmental sensor and the second scan of the second environmental sensor are combined to form a second fused scan. An aligning transformation between the first fused scan and the second fused scan is ascertained.

Abstract: A method for generating training datasets for training an evaluation algorithm using which an alignment of two map datasets can be evaluated to determine navigation information for a mobile device that is moving or is to move in an environment. The method includes, for each of a plurality of training datasets: providing two input feature datasets, wherein underlying map datasets each contain environmental information that has been acquired using a sensor of the mobile device; providing a transformation dataset generated during an alignment of the two input feature datasets; providing a reference transformation dataset as ground truth; determining a correlation dataset based on the two input feature datasets and/or the transformation dataset; determining a quality measure depending on an accuracy of a match between the transformation dataset and the reference transformation dataset; and providing the training dataset which includes the correlation dataset and the quality measure.

Abstract: A method for creating map data for the operation of an automated and/or assisting system of a vehicle or robot. A map for a traffic route portion of a traffic route is compared with a reference map for the traffic route portion on an automated basis. The map is used exclusively for creating the map data on an automated basis when the map does not deviate from the reference map by a predefined extent with regard to at least one predefined quality measure.

Abstract: A method for generating training datasets for training an evaluation algorithm using which an alignment of two map datasets can be evaluated to determine navigation information for a mobile device that is moving or is to move in an environment. The method includes, for each of a plurality of training datasets: providing two input feature datasets, wherein underlying map datasets each contain environmental information that has been acquired using a sensor of the mobile device; providing a transformation dataset generated during an alignment of the two input feature datasets; providing a reference transformation dataset as ground truth; determining a correlation dataset based on the two input feature datasets and/or the transformation dataset; determining a quality measure depending on an accuracy of a match between the transformation dataset and the reference transformation dataset; and providing the training dataset which includes the correlation dataset and the quality measure.

Abstract: A method and device for creating a digital map. The method includes: receiving first training data sets; generating second training data sets by means of a generative neural network by determining a second, perturbed image of the same surrounding area in each case for each first image, wherein each second training data set represents the second image of the corresponding first image; training a further neural network; receiving surrounding area data sets, which in each case represent a surrounding area image of a vehicle surrounding area, wherein these surrounding area data sets comprise a position description of the corresponding vehicle surrounding area; creating the digital map by merging the surrounding area images, depending on the position description, by means of a scan matching method, wherein the scan matching method includes at least the further neural network, and a step of providing the digital map.

Abstract: A method for aligning two map datasets. The method includes: providing two map datasets, each containing environmental information, wherein the environmental information in the two map datasets has been detected by a sensor of a mobile device, and at least one of the two map datasets is a sparse map dataset; providing the two map datasets as input feature data or determining input feature data based on the two map datasets; carrying out an alignment of the two map datasets using a machine learning algorithm based on sparse convolution, wherein output data including information about a transformative relation between the two map datasets are generated from the input feature data, via intermediate feature data in one or more intermediate layers.

Abstract: A method for aligning a first map section of a digital road map with a second map section of the digital road map that at least partially overlaps the first map section. The method includes: determining that a first relative rotation between the two map sections cannot be unambiguously ascertained; ascertaining a second relative rotation between a third map section of the digital road map and a fourth map section of the digital road map that at least partially overlaps the third map section, wherein the third map section and the fourth map section are adjacent to the first map section and to the second map section; aligning the first map section with the second map section; wherein the alignment includes ascertaining a relative rotation between the first map section and the second map section based on the ascertained second relative rotation.