Woven Planet North America, Inc.

Darko Zikic

Clemens Marschner

Wilhelm Richert

Thomas Schiwietz

Nikolai Morin

Marco Caccin

Examples disclosed herein may involve (i) obtaining 2D image data and 3D sensor data that is representative of an area, (ii) identifying a first set of pixels associated with ephemeral objects detected in the area and a second set of pixels associated with non-ephemeral objects detected in the area, (iii) identifying a first set of ephemeral 3D data points associated with the detected ephemeral objects and a second set of non-ephemeral 3D data points associated with the detected non-ephemeral objects, (iv) mapping the first and second sets of 3D data points to a grid of voxels associated with the area, (v) making a determination that one or more voxels in the grid each contain a threshold extent of ephemeral data points, and (vi) based at least in part on the determination, filtering the 3D sensor data to remove the 3D data points contained within the one or more voxels.

Systems and methods for filtering sensor data to remove data points associated with ephemeral objects

The present disclosure relates to a method of generating a ground map. For instance, the present disclosure provides a method of determining relevant point cloud data for generating a surface topology over real-world geographical areas using sensor data, which may involve (i) receiving a first dataset from a first sensor, (ii) receiving a second dataset from a second sensor, (iii) classifying the first dataset by identifying characteristics of the first dataset that correspond to a ground surface, (iv) filtering the second dataset based on the classified first dataset, and (v) generating a ground map from the filtered second dataset.

Ground map generation

The present invention relates to a method of generating an overhead view image of an area. More particularly, the present invention relates to a method of generating a contextual multi-image based overhead view image of an area using ground map data and field of view image data. Various embodiments of the present technology can include methods, systems and non-transitory computer readable media and computer programs configured to determine a ground map of the geographical area; receiving a plurality of images of the geographical area; process the plurality of images to select a subset of images to generate the overhead view of the geographical area; divide the ground map into a plurality of sampling points of the geographical area; and determine a color of a plurality of patches of the overhead view image from the subset of images, each patch representing each sampling point of the geographical area.

Generating training data from overhead view images

Various embodiments of the present technology can include methods, systems and non-transitory computer readable media and computer programs configured to determine one or more images of a generated overhead view of a geographical area. The generated overhead view is generated from aggregated pixel values determined from correlated pixel values in images of the geographical area. The disclosed approaches identify semantic map features as being present in the images of the overhead perspective of the geographic area. The disclosed approaches extract the semantic map features of the geographical area from the images of the substantially overhead perspective of the geographical area. The disclosed approaches translate the extracted semantic map features to a semantic map layer of a geometric map associated with the geographical area.

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Munich, Germany

Darko Zikic