Abstract: A computer-implemented method for generating a bird's eye view image of a scene includes: (a) acquiring at least one lidar frame comprising points with inherent distance information and at least one camera image of the scene; (b) generating a mesh representation of the scene by using the at least one lidar frame, the mesh representation representing surfaces shown in the scene with inherent distance information; (c) generating a mask image by classifying pixels of the at least one camera image as representing ground pixels or non-ground pixels of the at least one camera image; and (d) generating the bird's eye view image by enhanced inverse perspective mapping exploiting distance information inherent to the surfaces of the mesh representation, pixels of the mask image classified as ground pixels, and the at least one camera image.

Abstract: A computer-implemented method for automatically annotating sensor data includes: receiving a multiplicity of sensor data frames; annotating the multiplicity of sensor data frames using at least one neural network, wherein the annotating comprises assigning at least one data point to each sensor data frame and assigning at least one state attribute to each data point; grouping the data points on the basis of the at least one state attribute, wherein a first group comprises data points for which the at least one state attribute is in a defined value range; selecting a first sample of one or more data points from the first group; and determining a quality metric for the one or more data points in the first sample.

Abstract: A method for determining an ego pose of a mobile system and creating a surfel map of a surrounding area of the mobile system via an optimization problem represented by a factor graph includes the steps of: receiving environment sensor data generated by an environment sensor attached to the mobile system, wherein the environment sensor surveys the surrounding area of mobile system, and wherein the environment sensor data represent the surrounding area of the mobile system as a point cloud; generating surfels by converting the point cloud of the received environment sensor data into surfel data; identifying new surfels and known surfels in the generated surfels by comparing the surfel data with the surfel map; and adding a surfel factor for the known surfels to the factor graph and/or adding a surfel node and a surfel factor for the new surfels to the factor graph.

Abstract: A method for determining an ego pose of a mobile system and creating a surfel map of a surrounding area of the mobile system via an optimization problem represented by a factor graph includes the steps of: receiving environment sensor data generated by an environment sensor attached to the mobile system, wherein the environment sensor surveys the surrounding area of mobile system, and wherein the environment sensor data represent the surrounding area of the mobile system as a point cloud; generating surfels by converting the point cloud of the received environment sensor data into surfel data; identifying new surfels and known surfels in the generated surfels by comparing the surfel data with the surfel map; and adding a surfel factor for the known surfels to the factor graph and/or adding a surfel node and a surfel factor for the new surfels to the factor graph.

Abstract: A computer-implemented method for annotating driving scenario sensor data, including the steps of receiving raw sensor data, the raw sensor data comprising a plurality of successive LIDAR point clouds and/or a plurality of successive camera images, recognizing objects in each image of the camera data and/or each point cloud using one or more neural networks, correlating objects within successive images and/or point clouds, removing false positive results on the basis of plausibility criteria, and exporting the annotated sensor data of the driving scenario.

Abstract: A computer-implemented method for generating a bird’s eye view image of a scene includes: (a) acquiring at least one lidar frame comprising points with inherent distance information and at least one camera image of the scene; (b) generating a mesh representation of the scene by using the at least one lidar frame, the mesh representation representing surfaces shown in the scene with inherent distance information; (c) generating a mask image by classifying pixels of the at least one camera image as representing ground pixels or non-ground pixels of the at least one camera image; and (d) generating the bird’s eye view image by enhanced inverse perspective mapping exploiting distance information inherent to the surfaces of the mesh representation, pixels of the mask image classified as ground pixels, and the at least one camera image.

Abstract: The illustration may have a separate display window that displays illustrations which may be moving illustration related to the current spot on the map or to future spots on the map. The illustration may be viewed while traveling or may be viewed in advance. The moving illustration may display segments of the travel path with points of interest and substantial changes at a slow speed and/or low altitude and may display segments without points of interest and/or few substantial changes at a high speed and or high altitude.