Abstract: According to one or more embodiments, operations may comprise obtaining a first point cloud. The operations also comprise performing segmentation of the first point cloud, the segmentation generating one or more clusters of points of the point cloud. The operations also comprise determining, for each respective cluster of the plurality of clusters, a respective geometric feature of a corresponding object that corresponds to the respective cluster. The operations also comprise obtaining a second point cloud. The operations also comprise assigning a plurality of weights that comprises assigning a respective weight to each respective cluster based on the respective geometric feature that corresponds to the respective cluster. The operations also comprise obtaining a second point cloud and aligning the first point cloud with the second point cloud based on the plurality of weights.

Abstract: Operations may comprise obtaining a first point cloud from a map representing a region. The operations may also include obtaining a second point cloud from one or more sensors of a vehicle traveling through the region. In addition, the operations may include identifying one or more subsets of clusters of second points of the second point cloud. The operations may also include determining correspondences between first points of the first point cloud and cluster points of the one or more subsets of clusters of the second point cloud. Moreover, the operations may include identifying at least a cluster of the one or more subsets of clusters, the identified cluster having, with respect to first points of the first point cloud, a correspondence percentage that is less than a threshold value. The operations may also include adjusting the second point cloud based on the identified cluster.

Abstract: According to an aspect of an embodiment, operations may comprise receiving a search query for points near a query-point, accessing a compressed octree representation of a point cloud comprising 3D points of a region, and traversing the compressed octree representation to identify regions that overlap a search space by, marking a current node as overlapping the search space responsive to determining that the current node is a leaf node, identifying a child node of the current node and performing a nearest neighbor search in the child node responsive to determining that a region represented by the current node overlaps the search space, and identifying a sibling node of the current node and performing the nearest neighbor search in the sibling node responsive to determining that a region represented by the current node does not overlap the search space.

Abstract: According to an aspect of an embodiment, operations may comprise receiving, from a LIDAR mounted on a vehicle, a first 3D point cloud comprising points of a region around the vehicle as observed by the LIDAR. The operations may also comprise accessing an HD map comprising a second 3D point cloud comprising points of the region around the vehicle. The operations may also comprise segmenting LIDAR ground points from LIDAR non-ground points in the first 3D point cloud. The operations may also comprise segmenting map ground points from map non-ground points in the second 3D point cloud. The operations may also comprise determining a pose of the vehicle by matching the LIDAR ground points to the map ground points and by matching the LIDAR non-ground points to the map non-ground points.

Abstract: According to an aspect of an embodiment, operations may comprise receiving a 3D point cloud representation of a region comprising points, with each point of the 3D point cloud representation associated with a normal value of a surface corresponding to the point, storing a set of discretized normal values, for each point of the 3D point cloud representation, associating the point with one of the discretized normal values in the set of discretized normal values by mapping the normal value associated with the point to the one of the discretized normal values in the set of discretized normal values, and storing a compressed octree representation comprising nodes, with at least a subset of the nodes of the compressed octree representation storing an index value identifying a discretized normal value for points of the 3D point cloud representation represented by the node.

Abstract: According to an aspect of an embodiment, operations may comprise receiving an approximate geographic location of a vehicle, accessing a map of a region within which the approximate geographic location of the vehicle is located, identifying a first region on the map within a first threshold distance of the approximate geographic location of the vehicle, identifying a second region on the map associated with one or more roads on the map, determining a search space on the map within which the vehicle is likely to be present, the search space representing an intersection of the first region and the second region, and determining a more accurate geographic location of the vehicle by performing a search within the search space.

Abstract: An autonomous vehicle system removes ephemeral points from lidar samples. The system receives a plurality of light detection and ranging (lidar) samples captured by a lidar sensor. Along with the lidar samples, the system receives an aligned pose and an unwinding transform for each of the lidar samples. The system determines one or more occupied voxel cells in a three-dimensional (3D) space using the lidar samples, their aligned poses, and their unwinding transforms. The system identifies occupied voxel cells representative of noise associated with motion of an object relative to the lidar sensor. The system filters the occupied voxel cells by removing the cells representative of noise. The system inputs the filtered occupied voxel cells in a 3D map comprising voxel cells, e.g., during the map generation and/or a map update.

Abstract: A system align point clouds obtained by sensors of a vehicle using kinematic iterative closest point with integrated motions estimates. The system receives lidar scans from a lidar mounted on the vehicle. The system derives point clouds from the lidar scan data. The system iteratively determines velocity parameters that minimize an aggregate measure of distance between corresponding points of the plurality of pairs of points. The system iteratively improves the velocity parameters. The system uses the velocity parameters for various purposes including for building high definition maps used for navigating the vehicle.

Abstract: 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.

Abstract: 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.

Abstract: A method may include projecting, onto a first projection plane of a first projection volume, first points from a point cloud of a setting that are within the first projection volume. Further, the method may include matching a plurality of the projected first points with a cross-section template that corresponds to a line parametric object (LPO) of the setting to determine a plurality of first element points of a first primary projected element. Additionally, the method may include projecting, onto a second projection plane of a second projection volume, second points from the point cloud that are within the second projection volume and matching a plurality of the projected second points with the cross-section template to determine a plurality of second element points of a second primary projected element. Moreover, the method may include generating a parameter function based on the first element points and the second element points.

Abstract: A method may include projecting, onto a first projection plane of a first projection volume, first points from a point cloud of a setting that are within the first projection volume. Further, the method may include matching a plurality of the projected first points with a cross-section template that corresponds to a line parametric object (LPO) of the setting to determine a plurality of first element points of a first primary projected element. Additionally, the method may include projecting, onto a second projection plane of a second projection volume, second points from the point cloud that are within the second projection volume and matching a plurality of the projected second points with the cross-section template to determine a plurality of second element points of a second primary projected element. Moreover, the method may include generating a parameter function based on the first element points and the second element points.