Here Global B.v.

David Doria

Xin Chen

Fangning He

Engin Burak Anil

Charles Morcom

Pamela T Korda

Zichen Li

Belle Walker

Uyen Nguyen

Daniel Weinstein

Phillip Bernard

Ralf Herrtwich

Matthew Emery Goss

Nezih Ergin Ozkucur

Ian Endres

Hank Sutton

Coby Spolin Parker

Peter Forbes

David Vandebunte

Bin Chen

An apparatus for sign detection includes a point cloud analysis module, an image analysis module, a frustum comparison module, and a sign detector. The point cloud analysis module is configured to receive point cloud data associated with a geographic region and classify at least one point neighborhood in the point cloud data as planar and a sign position candidate. The image analysis module is configured to receive image data associated with the geographic region and calculate a sighting frustum from the image data. The frustum comparison module is configured to perform a comparison of the sighting frustum to the sign position candidate having at least one point neighborhood classified as planar. The sign detector is configured to provide a location for the sign detection in response to the comparison of the sighting frustum to the sign position candidate.

Automatic positioning of 2D image sign sightings in 3D space

Methods, apparatuses, and systems are provided for detecting overhead obstructions along a path segment. One exemplary method includes receiving three-dimensional data collected by a depth sensing device traveling along a path segment, wherein the three-dimensional data comprises point cloud data positioned above a ground plane of the path segment. The method further includes identifying data points of the point cloud data positioned within a corridor positioned above the ground plane. The method further includes projecting the identified data points onto a plane. The method further includes detecting the overhead obstruction based on a concentration of point cloud data positioned within a plurality of cells of the plane. The method further includes storing the detected overhead obstruction above the path segment within a map database.

Automatic detection of overhead obstructions

A method for road surface detection includes receiving ranging data including a plurality of ranging data points, extracting one or more ranging data points lying within a height range from the plurality of ranging data points, dividing the one or more ranging data points into one or more grid cells, setting a first horizontal position of a first cell point of a first grid cell of the one or more grid cells as being centered on the first grid cell, setting a first vertical position of the first cell point, and detecting the road surface based on the first vertical position and first horizontal position of the first cell point.

Road surface detection

An apparatus and method automatically detects and positions structure faces. After receiving data points describing a geographical area, neighborhoods are defined based on the data points and classified as linear, planar, or volumetric. Neighborhoods are merged into at least one cluster based on local surface normals. At least one bounding frame is fit to the at least one cluster and modified based on a field of interest.

Automatic detection and positioning of structure faces

Embodiments include apparatus and methods for automatic detection of pole-like objects for a location at a region of a roadway and automatic localization based on the detected pole-like objects. Pole-like objects are modeled as cylinders and the models are generated based on detected vertical clusters of point cloud data associated to corresponding regions along the region of the roadway. The modeled pole-like objects are stored in a database and associated with the region of the roadway. Sensor data from a user located at the region of the roadway is received. The pole-like object model is accessed and compared to the received sensor data. Based on the comparison, localization of the user located at the region of the roadway is performed.

Automatic detection and positioning of pole-like objects in 3D

Embodiments include apparatus and methods for automatic generation of object polylines that represent roadside objects at a region of a roadway and automatic localization based on the object polylines. Object polylines are generated based on occupied voxels closest to the roadway of one or more voxel occupancy grids associated with the region of the roadway. The object polylines are stored in a database and associated with the region of the roadway. Sensor data from a user located at the region of the roadway is received. The accessed object polylines and the received sensor data are compared. Based on the comparison, localization of the user located at the region of the roadway is performed.

Automatic identification of roadside objects for localization

An apparatus, method and computer program product are provided to facilitate the navigation of a vehicle, such as an autonomous vehicle, utilizing map data in which the quality associated with the map data is provided in a more computationally efficient manner. In the context of a method a plurality of different types of sensor data are received including map data, camera data and detector data. The method determines a quality index associated with the map data and weights the reliance upon the map data relative to other types of sensor data based upon the quality index associated with the map data. The method further includes determining navigation information for the vehicle based at least partly upon the weighting of the map data relative to other types of sensor data.

Apparatus, method and computer program product for facilitating navigation of a vehicle based upon a quality index of the map data

Embodiments include apparatus and methods for generating a localization geometry or occupancy grid for a geographic location. Point cloud that describes a vicinity of a pathway is collected by a distance sensor and describing a vicinity of the pathway. The point cloud data is reduced or filtered to a predetermined volume with respect to the roadway. The remaining point cloud data is projected onto a two-dimensional plane including at least one pixel formation. A volumetric grid is defined according to the at least one pixel formation, and a voxel occupancy for each of a voxels forming the volumetric grid is determined. The arrangement of the voxel occupancies or a sequence of data describing the voxel occupancies is a localization geometry that describes the geographic location of the pathway.

Automatic localization geometry detection

A method for constructing a coordinate system relative to a vehicle traveling along a path includes receiving location data, dividing the path into path segments, matching a portion of the location data to a path segment, and determining a registration of the portion of the location data relative to the path segment. The registration may be organized by non-cartesian coordinates relative to a path point lying on the path segment.

Segmented path coordinate system

A geographic database storing map data is provided. The geographic database is stored in a non-transitory computer readable medium. The geographic database comprises a plurality of records corresponding to drivable surfaces of a road network. The plurality of records comprise a plurality of lane records corresponding to particular lanes of the road network. Each first record of the plurality of records comprises a plurality of instances of adjacency information. Each instance of adjacency information/data (a) links the first record corresponding to a first drivable surface of the road network to a second record of the plurality of records corresponding to a second drivable surface of the road network. The first drivable surface is adjacent to the second drivable surface. Each instance of adjacency information/data indicates crossing parameters between the first drivable surface and the second drivable surface.

Lane-centric road network model for navigation

Embodiments include apparatus and methods for automatic generation of local window-based 2D occupancy grids that represent roadside objects at a region of a roadway and automatic localization based on the 2D occupancy grids. 2D occupancy grids are generated based on an altitude threshold for point cloud data and grid cell occupancy for grid cells of local windows associated with the region of the roadway. The 2D occupancy grids are stored in a database and associated with the region of the roadway. Sensor data from a user located at the region of the roadway is received. The accessed 2D occupancy grids and the received sensor data are compared. Based on the comparison, localization of the user located at the region of the roadway is performed.

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Oak Park, IL, United States of America

David Doria