Abstract: Systems and methods are disclosed related to generating an interactive user interface that enables a user to move, rotate or otherwise edit 3D point cloud data in virtual 3D space to align or match point clouds captured from LiDAR scans prior to generation of a high definition map. A system may obtain point cloud data for two or more point clouds, render the point clouds for display in a user interface, then receive a user selection of one of the point clouds and commands from the user to move and/or rotate the selected point cloud. The system may adjust the displayed position of the selected point cloud relative to the other simultaneously displayed point cloud(s) in real time in response to the user commands, and store the adjusted point cloud position data for use in generating a new high definition map.

Abstract: The present disclosure is related to systems and methods for positioning. The method includes obtaining estimated pose data of a subject. The method also includes generating a local map associated with the estimated pose data. The method also includes obtaining, based on the estimated pose data, a reference map. The method also includes correlating the local map and the reference map in a frequency domain. The method further includes determining, based on the estimated pose data and the correlation between the local map and the reference map in the frequency domain, target pose data of the subject.

Abstract: The present disclosure is directed to systems and methods for performing object detection and pose estimation in 3D from 2D images. Object detection can be performed by a machine-learned model configured to determine various object properties. Implementations according to the disclosure can use these properties to estimate object pose and size.

Abstract: A method includes receiving a video comprising images representing an object, and determining, using a machine learning model, based on a first image of the images, and for each respective vertex of vertices of a bounding volume for the object, first two-dimensional (2D) coordinates of the respective vertex. The method also includes tracking, from the first image to a second image of the images, a position of each respective vertex along a plane underlying the bounding volume, and determining, for each respective vertex, second 2D coordinates of the respective vertex based on the position of the respective vertex along the plane. The method further includes determining, for each respective vertex, (i) first three-dimensional (3D) coordinates of the respective vertex based on the first 2D coordinates and (ii) second 3D coordinates of the respective vertex based on the second 2D coordinates.

Abstract: Systems and methods involving obtaining point cloud data from one or more sources corresponding to one or more zones within a real-world space, the point cloud data representing surface features of structures detected within the one or more zones; defining a plurality of first-level nodes based on the point cloud data, individual first-level nodes corresponding to obtained point cloud data corresponding to individual zones of the one or more zones; identifying connections between two or more first-level nodes, the connections between the two or more first-level nodes based on connections between the point cloud data for the zones corresponding to the two or more first-level nodes; and defining a plurality of second-level nodes, individual second-level nodes corresponding to aggregated subsets of first-level nodes for which connections are identified.

Abstract: The present disclosure is directed to systems and methods for generating synthetic training data using augmented reality (AR) techniques. For example, images of a scene can be used to generate a three-dimensional mapping of the scene. The three-dimensional mapping may be associated with the images to indicate locations for positioning a virtual object. Using an AR rendering engine, implementations can generate an and orientation. The augmented image can then be stored in a machine learning dataset and associated with a label based on aspects of the virtual object.

Abstract: Example embodiments allow for fast, efficient determination of bounding box vertices or other pose information for objects based on images of a scene that may contain the objects. An artificial neural network or other machine learning algorithm is used to generate, from an input image, a heat map and a number of pairs of displacement maps. The location of a peak within the heat map is then used to extract, from the displacement maps, the two-dimensional displacement, from the location of the peak within the image, of vertices of a bounding box that contains the object. This bounding box can then be used to determine the pose of the object within the scene. The artificial neural network can be configured to generate intermediate segmentation maps, coordinate maps, or other information about the shape of the object so as to improve the estimated bounding box.

Abstract: The present disclosure relates to a method for determining a pose of a subject. The method may include identifying a plurality of sets of data points representing a plurality of cross sections of a path from point-cloud data representative of a surrounding environment, wherein the plurality of cross sections may be perpendicular to the ground surface and distributed along a first reference direction associated with the subject. The method may also include determining a feature vector of the at least one curb based on the plurality of sets of data points, determining at least one reference feature vector of the at least one curb based on an estimated pose of the subject and a location information database, and determining the pose of the subject by updating the estimated pose of the subject.

Abstract: A method includes receiving a video comprising images representing an object, and determining, using a machine learning model, based on a first image of the images, and for each respective vertex of vertices of a bounding volume for the object, first two-dimensional (2D) coordinates of the respective vertex. The method also includes tracking, from the first image to a second image of the images, a position of each respective vertex along a plane underlying the bounding volume, and determining, for each respective vertex, second 2D coordinates of the respective vertex based on the position of the respective vertex along the plane. The method further includes determining, for each respective vertex, (i) first three-dimensional (3D) coordinates of the respective vertex based on the first 2D coordinates and (ii) second 3D coordinates of the respective vertex based on the second 2D coordinates.

Abstract: The present disclosure relates to systems and methods for determining a target position of a target subject. The method may include determining, via a positioning device, an initial position of a target subject in real-time. The method may also include determining, via a data capturing device, first data indicative of a first environment associated with the initial position of the target subject and determining a first map based on the first data indicative of the first environment. The first map may include reference feature information of at least one reference object with respect to the first environment. The method may also include determining a target position of the target subject based on the initial position, the first map, and a second map in real-time. The second map may include second data indicative of a second environment corresponding to an area including the initial position of the target subject.

Abstract: The present disclosure relates to systems and methods for determining a target position of a target subject. The method may include determining an initial position of a target subject in real-time. The method may also include determining a plurality of images indicative of a first environment associated with the initial position of the target subject. Further, the method may include determining a first map based on the plurality of images. The first map may include first map data indicative of the first environment associated with the initial position of the target subject. The method may also include determining a target position of the target subject based on the initial position, the first map, and a second map in real-time. The second map may include second map data indicative of a second environment corresponding to an area including the initial position of the target subject.

Abstract: The present disclosure is related to systems and methods for positioning. The method includes obtaining estimated pose data of a subject. The method also includes generating a local map associated with the estimated pose data. The method also includes obtaining, based on the estimated pose data, a reference map. The method also includes correlating the local map and the reference map in a frequency domain. The method further includes determining, based on the estimated pose data and the correlation between the local map and the reference map in the frequency domain, target pose data of the subject.

Abstract: The present disclosure relates to positioning systems and methods. A system may obtain point-cloud data acquired by one or more sensors associated with a subject during a time period. The point-cloud data may be associated with an initial position of the subject. The system may also divide the point-cloud data into a plurality of groups. The system may also obtain pose data of the subject corresponding to each group of the plurality of groups of the point-cloud data. The system may also register, based on the pose data of the subject, the each group of the plurality of groups of the point-cloud data to form registered point-cloud data. The system may also generate, based on the registered point-cloud data, a local map associated with the initial position of the subject.

Abstract: Systems and methods involving obtaining point cloud data from one or more sources corresponding to one or more zones within a real-world space, the point cloud data representing surface features of structures detected within the one or more zones; defining a plurality of first-level nodes based on the point cloud data, individual first-level nodes corresponding to obtained point cloud data corresponding to individual zones of the one or more zones; identifying connections between two or more first-level nodes, the connections between the two or more first-level nodes based on connections between the point cloud data for the zones corresponding to the two or more first-level nodes; and defining a plurality of second-level nodes, individual second-level nodes corresponding to aggregated subsets of first-level nodes for which connections are identified.

Abstract: Technologies are disclosed that determine when alternative routes are to be pre-generated for a vehicle. A route planning system receives a request for a navigation route for a vehicle. The navigation route is generated, where the navigation route includes navigation nodes. For a given navigation node, a corresponding weight and/or a probability of an exception event occurring are determined, and are used to determine whether to generate an alternate route configured to be utilized in response to a future detection by the vehicle of an exception event associated with the given node. The route planning system may then generate alternate routes accordingly, and may transmit the navigation route and an alternate route for a corresponding given node to the vehicle. The navigation route may be used to navigate the vehicle, and the alternate route may be used to navigate the vehicle if an exception event is detected.

Abstract: Example embodiments allow for fast, efficient determination of bounding box vertices or other pose information for objects based on images of a scene that may contain the objects. An artificial neural network or other machine learning algorithm is used to generate, from an input image, a heat map and a number of pairs of displacement maps. The location of a peak within the heat map is then used to extract, from the displacement maps, the two-dimensional displacement, from the location of the peak within the image, of vertices of a bounding box that contains the object. This bounding box can then be used to determine the pose of the object within the scene. The artificial neural network can be configured to generate intermediate segmentation maps, coordinate maps, or other information about the shape of the object so as to improve the estimated bounding box.

Abstract: Technologies are disclosed that are used to control the transmission and processing of vehicle telemetry data. Routing data configured to be used to navigate the vehicle is accessed. Using the routing data, telemetry broadcast parameters are generated. The telemetry broadcast parameters are transmitted to the vehicle. Transmissions of telemetry data in accordance with the broadcast parameters are received from the vehicle. The received telemetry data is used in generating a simulated environment for a route planning learning engine, in simulating a vehicle, in validating routes and maps, and/or in performing vehicle diagnostics.

Abstract: Systems and methods are disclosed related to generating an interactive user interface that enables a user to move, rotate or otherwise edit 3D point cloud data in virtual 3D space to align or match point clouds captured from LiDAR scans prior to generation of a high definition map. A system may obtain point cloud data for two or more point clouds, render the point clouds for display in a user interface, then receive a user selection of one of the point clouds and commands from the user to move and/or rotate the selected point cloud. The system may adjust the displayed position of the selected point cloud relative to the other simultaneously displayed point cloud(s) in real time in response to the user commands, and store the adjusted point cloud position data for use in generating a new high definition map.

Abstract: Systems and processes can reduce an amount of training data used to generate a machine learning model while maintaining or improving a resultant of the machine learning model. The amount of training data may be reduced by pre-processing the training data to normalize the training data. The training data may include images of portions of an elongated object, such as a road. Each of the images can be normalized by, for example, rotating each of the images such that the depicted roads are horizontal or otherwise share the same angle. By aligning disparate images of roads, it is possible to reduce the amount of training data and to increase the accuracy of the machine learning model. Further, the use of normalized images by the machine learning model enables a reduction in computing resources used to apply data to the machine learning model to, for example, identify lane markings within images.

Abstract: A method and systems for loading and tracking maps on a moving vehicle. One method includes obtaining a geographic location of a system on a vehicle, obtaining a boundary corresponding to a contiguous geographical boundary area around the geographic location of the system, loading map data comprising a plurality of map data tiles each including a portion of the geographical boundary area, the plurality of map data tiles including a center tile having a point corresponding to the system location and surrounding map data tiles. The method further includes obtaining an updated system location, and if the updated geographic location is outside of the boundary area, obtaining an updated boundary centered on the updated geographic location and loading map data based on the updated boundary such that the resulting loaded map data includes a center tile and map data tiles surrounding the center tile that intersect the geographical boundary area.