Abstract: A method for localizing a motor vehicle in an environment during a driving operation comprises, by a processor circuit in repeated estimation cycles, receiving sensor data of landmarks of the environment from an environment sensor, and ascertaining a respective estimated position of the motor vehicle from feature data, which are formed of map data of a map region of the environment and of the sensor data, using an estimation module. A movement path is estimated from the positions of multiple of the estimation cycles using a statistical observer model, and the observer model, during the formation of the movement path, models measurement noise contained in the position data as a covariance matrix of position coordinates of the position data, with matrix values of the covariance matrix being ascertained by the estimation module as a function of the sensor data and the map data.

Abstract: A method for training an artificial neural generator network for the generating of synthetic landmark images is provided, in which landmark images are extracted from at least one training map as training data, which form a first training data set, and the generator network as a generator of a generative adversarial network learns with the aid of the first training data set and with the aid of real, non-annotated image data, recorded by a sensor device, to generate synthetic landmark images which are suited to reproducing the probability distribution underlying the first training data set. The invention also relates to a method for training an artificial neural localization network by the trained generator network and a method for determining a position of a mobile unit with at least one sensor device and at least one environment map by the trained localization network.

Abstract: Systems and methods of evaluating parking spaces are provided, and in particular evaluating parking spaces in parking lots or parking garages, with regard to autonomous driving. This is accomplished by determining an accessible area of the parking space, and defining grid points in the accessible area. Features of the parking space are determined for each of the grid points, where each of the features is registrable from a respective one of the grid points. A quality value is obtained from an average number of features that is registrable from each of the grid points.

Abstract: According to a method for self-localization of a vehicle, a first pose of the vehicle is determined in a map coordinates system, based on environment sensor data representing an environment of the vehicle, a landmark is detected in the environment, a position of the landmark is determined in the map coordinates system and a second pose of the vehicle is determined in the map coordinates system dependent on the position of the landmark. An assignment instruction is consulted, matching up the first pose with at last one preferred sensor type or at least one dominant landmark type. Depending on the assignment instruction, a first environment sensor system is activated and a second environment sensor system is deactivated, whereupon the environment sensor data are generated by means of the first environment sensor system.

Abstract: According to a method for self-localization of a vehicle, a first pose of the vehicle is determined. Environment sensor data are generated by means of an environment sensor device and on the basis of this a landmark is detected in the environment. A position of the landmark is determined and in dependence on this a second pose of the vehicle is determined. A memorized assignment instruction is consulted, matching up the first pose with at last one preferred sensor type or at least one dominant landmark type, and depending on this a first portion of the environment sensor data is selected that was generated by means of a first environment sensor system of the environment sensor device configured according to a first sensor type. For the detecting of the landmark, a first landmark detection algorithm is applied to the first portion of the environment sensor data.

Abstract: A method for training an artificial neural generator network for the generating of synthetic landmark images is provided, in which landmark images are extracted from at least one training map as training data, which form a first training data set, and the generator network as a generator of a generative adversarial network learns with the aid of the first training data set and with the aid of real, non-annotated image data, recorded by a sensor device, to generate synthetic landmark images which are suited to reproducing the probability distribution underlying the first training data set. The invention also relates to a method for training an artificial neural localization network by the trained generator network and a method for determining a position of a mobile unit with at least one sensor device and at least one environment map by the trained localization network.

Abstract: A method is described in which a position of a vehicle is determined in a road network, having a plurality of edges, each of them being associated with a road section or a driving lane on a road section, by means of a sensor group of the vehicle, wherein first sensor data are detected by means of the sensor group, on the basis of the first sensor data each edge of a plurality of first edges of the road network is associated with a first probability that the vehicle is located in the road section associated with the first edge or in the driving lane associated with the first edge, second sensor data are detected by means of the sensor group, on the basis of the second sensor data and the probabilities associated with the first edges, a second probability is associated with each edge of a plurality of second edges of the road network that the vehicle is located in the road section associated with the second edge or in the driving lane associated with the second edge, and the second edge with the highest associa

Abstract: A method for localizing a motor vehicle in an environment during a driving operation comprises, by a processor circuit in repeated estimation cycles, receiving sensor data of landmarks of the environment from an environment sensor, and ascertaining a respective estimated position of the motor vehicle from feature data, which are formed of map data of a map region of the environment and of the sensor data, using an estimation module. A movement path is estimated from the positions of multiple of the estimation cycles using a statistical observer model, and the observer model, during the formation of the movement path, models measurement noise contained in the position data as a covariance matrix of position coordinates of the position data, with matrix values of the covariance matrix being ascertained by the estimation module as a function of the sensor data and the map data.

Abstract: A method for determining the pose of a mobile unit having at least one sensor device, which is designed to register environment images of the mobile unit, and a map, in which at least one passable region is marked, includes registering at least one environment image by of the at least one sensor device and using the at least one registered environment image to calculate at least one collision-free region, and determining the pose of the mobile unit by a comparison of the at least one calculated collision-free region with the at least one passable region marked on the map. Furthermore, a system for carrying out the method is also described.

Abstract: To determine an absolute position of a vehicle, the vehicle is moved along a trajectory starting from the absolute initial position. The trajectory is sensed by odometry starting from a defined initial position, which is associated with the absolute initial position. Three objects are detected from the vehicle which is on the trajectory. At each detection, a detection beam is sensed. The trajectory with the detection beams are matched with a map. Finally, the absolute initial position is determined from the matching.

Abstract: According to a method for self-localization, a vehicle is detected by a vehicle-external sensor system of a parking infrastructure when it is located in a detection region of the vehicle-external sensor system. By way of a vehicle-external communication interface of the parking infrastructure, position information regarding the detection region is relayed in dependence on the detection of the vehicle to at least one vehicle computer unit and an initial pose of the vehicle is determined by the at least one vehicle computer unit in dependence on the position information.

Abstract: According to a method for self-localization of a vehicle, a first pose of the vehicle is determined. Environment sensor data are generated by means of an environment sensor device and on the basis of this a landmark is detected in the environment. A position of the landmark is determined and in dependence on this a second pose of the vehicle is determined. A memorized assignment instruction is consulted, matching up the first pose with at last one preferred sensor type or at least one dominant landmark type, and depending on this a first portion of the environment sensor data is selected that was generated by means of a first environment sensor system of the environment sensor device configured according to a first sensor type. For the detecting of the landmark, a first landmark detection algorithm is applied to the first portion of the environment sensor data.

Abstract: According to a method for self-localization of a vehicle, a first pose of the vehicle is determined in a map coordinates system, based on environment sensor data representing an environment of the vehicle, a landmark is detected in the environment, a position of the landmark is determined in the map coordinates system and a second pose of the vehicle is determined in the map coordinates system dependent on the position of the landmark. An assignment instruction is consulted, matching up the first pose with at last one preferred sensor type or at least one dominant landmark type. Depending on the assignment instruction, a first environment sensor system is activated and a second environment sensor system is deactivated, whereupon the environment sensor data are generated by means of the first environment sensor system.

Abstract: A method for determining a position of a mobile unit with at least one sensor device, which is set up to capture environmental images of the mobile unit, and with a map, which comprises at least known landmarks with map position information. At least one hypothesis is determined for the at least one initially estimated position of the mobile unit and the at least one landmark detected in an environmental image, wherein the at least one hypothesis comprises at least one association of the at least one detected landmark of the at least one captured environmental image with at least one known landmark of the map. At least one new estimate of the position of the mobile unit and a weight of the at least one hypothesis are determined for the at least one hypothesis by means of at least one Kalman filter.

Abstract: A method for determining a position of a mobile unit with at least one sensor device, which is set up to capture environmental images of the mobile unit, and with a map, which comprises at least known landmarks with map position information. At least one hypothesis is determined for the at least one initially estimated position of the mobile unit and the at least one landmark detected in an environmental image, wherein the at least one hypothesis comprises at least one association of the at least one detected landmark of the at least one captured environmental image with at least one known landmark of the map. At least one new estimate of the position of the mobile unit and a weight of the at least one hypothesis are determined for the at least one hypothesis by means of at least one Kalman filter.

Abstract: A method for automatically generating a three-dimensional reference model as terrain information for a seeker head of an unmanned missile. A three-dimensional terrain model formed from model elements obtained with the aid of satellite and/or aerial reconnaissance is provided. Position data of the imaging device at least at one planned position and a direction vector from the planned position of the imaging device to a predetermined target point in the three-dimensional terrain model are provided. A three-dimensional reference model of the three dimensional terrain model is generated that incorporates only those model elements and sections of model elements from the terrain model, which in the viewing direction of the direction vector from the planned position of the imaging device, are not covered by other model elements and/or are not located outside the field of view of the imaging device.

Abstract: A method for determining the position data of a target object in a reference system from an observation position at a distance. A three-dimensional reference model of the surroundings of the target object is provided, the reference model including known geographical location data. An image of the target object and its surroundings, resulting from the observation position for an observer, is matched with the reference model. The position data of the sighted target object in the reference model is determined as relative position data with respect to known location data of the reference model.

Abstract: A SAR image recorded by a reconnaissance system is transferred as a reference edge image together with the data of the trajectory as a reference. The signal of the infrared seeker head of the missile is converted into a virtual SAR edge image and compared to the SAR reference image to calculate the precise position of the missile.

Abstract: A method for automatically generating a three-dimensional reference model as terrain information for a seeker head of an unmanned missile. A three-dimensional terrain model formed from model elements obtained with the aid of satellite and/or aerial reconnaissance is provided. Position data of the imaging device at least at one planned position and a direction vector from the planned position of the imaging device to a predetermined target point in the three-dimensional terrain model are provided. A three-dimensional reference model of the three dimensional terrain model is generated that incorporates only those model elements and sections of model elements from the terrain model, which in the viewing direction of the direction vector from the planned position of the imaging device, are not covered by other model elements and/or are not located outside the field of view of the imaging device.

Abstract: A method for determining the position data of a target object in a reference system from an observation position at a distance. A three-dimensional reference model of the surroundings of the target object is provided, the reference model including known geographical location data. An image of the target object and its surroundings, resulting from the observation position for an observer, is matched with the reference model. The position data of the sighted target object in the reference model is determined as relative position data with respect to known location data of the reference model.