A high-definition map system receives sensor data from vehicles travelling along routes and combines the data to generate a high definition map for use in driving vehicles, for example, for guiding autonomous vehicles. A pose graph is built from the collected data, each pose representing location and orientation of a vehicle. The pose graph is optimized to minimize constraints between poses. Points associated with surface are assigned a confidence measure determined using a measure of hardness/softness of the surface. A machine-learning-based result filter detects bad alignment results and prevents them from being entered in the subsequent global pose optimization. The alignment framework is parallelizable for execution using a parallel/distributed architecture. Alignment hot spots are detected for further verification and improvement. The system supports incremental updates, thereby allowing refinements of subgraphs for incrementally improving the high-definition map for keeping it up to date.

A method includes generating calibration data by geometrically calibrating first image data from a first camera unit relative to second image data from a second camera unit based on first descriptor data and second descriptor data. The first descriptor data is based on the first image data. The second descriptor data is based on the second image data. The method includes identifying, based on the calibration data, a target location relative to the first image data. The method further includes generating an output image that includes the first image data and an indication of where the target location is relative to a scene depicted in the first image data.

A method includes generating calibration data by geometrically calibrating first image data from a first camera unit relative to second image data from a second camera unit based on first descriptor data and second descriptor data. The first descriptor data is based on the first image data. The second descriptor data is based on the second image data. The method includes identifying, based on the calibration data, a target location relative to the first image data. The method further includes generating an output image that includes the first image data and an indication of where the target location is relative to a scene depicted in the first image data.

The present invention discloses a positioning method and apparatus. The method includes: acquiring a first image captured by an optical device, where the first image includes an observation object and a plurality of predetermined objects, and the predetermined objects are objects with known geographic coordinates; selecting a first predetermined object from the predetermined objects based on the first image; acquiring a second image, where the first predetermined object is located in a center of the second image; determining a first attitude angle of the optical device based on the first predetermined object in the second image and measurement data captured by an inertial navigation system; modifying the first attitude angle based on a positional relationship between the observation object and the first predetermined object in the second image, to obtain a second attitude angle; and calculating geographic coordinates of the observation object based on the second attitude angle. According to the present invention, a prior-art technical problem that costs of accurately locating an observation object are high is resolved.

The present invention discloses a positioning method and apparatus. The method includes: acquiring a first image captured by an optical device, where the first image includes an observation object and a plurality of predetermined objects, and the predetermined objects are objects with known geographic coordinates; selecting a first predetermined object from the predetermined objects based on the first image; acquiring a second image, where the first predetermined object is located in a center of the second image; determining a first attitude angle of the optical device based on the first predetermined object in the second image and measurement data captured by an inertial navigation system; modifying the first attitude angle based on a positional relationship between the observation object and the first predetermined object in the second image, to obtain a second attitude angle; and calculating geographic coordinates of the observation object based on the second attitude angle. According to the present invention, a prior-art technical problem that costs of accurately locating an observation object are high is resolved.

Embodiments of systems and methods for a mobile mapping system are described. In an embodiment, a method includes capturing a plurality of images of an object point using a mobile computing platform. The method may also include determining an initial set of orientation parameters in response to one or more orientation sensors on the mobile computing platform. Additionally, the method may include calculating a corrected set of orientation parameters by matching object points in the plurality of images. Further, the method may include estimating a three-dimensional ground coordinate associated with the captured images in response to the corrected set of orientation parameters.

Embodiments of systems and methods for a mobile mapping system are described. In an embodiment, a method includes capturing a plurality of images of an object point using a mobile computing platform. The method may also include determining an initial set of orientation parameters in response to one or more orientation sensors on the mobile computing platform. Additionally, the method may include calculating a corrected set of orientation parameters by matching object points in the plurality of images. Further, the method may include estimating a three-dimensional ground coordinate associated with the captured images in response to the corrected set of orientation parameters.

There is provided an image processing apparatus and an image processing method capable of robust correction to an image misalignment generated due to an over-time misalignment of a stereo camera. The estimation section estimates at least two parameters out of a pitch angle difference, a yaw angle difference, and a roll angle difference between a left camera and a right camera, and a scale ratio of a left image picked up by the left camera to a right image picked up by the right camera, on basis of a model formula using the parameters. The present disclosure is applicable to, for example, an imaging apparatus that includes a stereo camera configured with the left camera and the right camera and the like.

A vehicle computing system performs enhances relatively sparse data collected by a LiDAR sensor by increasing the density of points in certain portions of the scan. For instance, the system generates 3D triangles based on a point cloud collected by the LiDAR sensor and filters the 3D triangles to identify a subset of 3D triangles that are proximate to the ground. The system interpolates points within the subset of 3D triangles to identify additional points on the ground. As another example, the system uses data collected by the LiDAR sensor to identify vertical structures and interpolate additional points on those vertical structures. The enhanced data can be used for a variety of applications related to autonomous vehicle navigation and HD map generation, such as detecting lane markings on the road in front of the vehicle or determining a change in the vehicle's position and orientation.

A vehicle computing system performs enhances relatively sparse data collected by a LiDAR sensor by increasing the density of points in certain portions of the scan. For instance, the system generates 3D triangles based on a point cloud collected by the LiDAR sensor and filters the 3D triangles to identify a subset of 3D triangles that are proximate to the ground. The system interpolates points within the subset of 3D triangles to identify additional points on the ground. As another example, the system uses data collected by the LiDAR sensor to identify vertical structures and interpolate additional points on those vertical structures. The enhanced data can be used for a variety of applications related to autonomous vehicle navigation and HD map generation, such as detecting lane markings on the road in front of the vehicle or determining a change in the vehicle's position and orientation.