Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate long range localization. One of the methods includes obtaining, for a particular location of a vehicle having a camera and a detection sensor, surfel data including a plurality of surfels. Each surfel in the surfel data has a respective location and corresponds to a different respective detected surface in an environment. Image data captured by the camera is obtained. It is determined that a region of interest for detecting objects for a vehicle planning process is outside a detectable region for the detection sensor. In response, it is determined that the image data for the region of interest matches surfel color data for the surfels corresponding to the region of interest. In response, the vehicle planning process is performed with the region of interest designated as having no unexpected objects.

Abstract: Aspects and implementations of the present disclosure address shortcomings of existing technology by enabling autonomous vehicle simulations based on retro-reflection optical data. The subject matter of this specification can be implemented in, among other things, a method that involves initiating a simulation of an environment of an autonomous driving vehicle, the simulation including a plurality of simulated objects, each having an identification of a material type of the respective object. The method can further involve accessing simulated reflection data based on the plurality of simulated objects and retro-reflectivity data for the material types of the simulated objects, and determining, using an autonomous vehicle control system for the autonomous vehicle, a driving path relative to the simulated objects, the driving path based on the simulated reflection data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for the generation and use of a surfel map with semantic labels. One of the methods includes receiving a surfel map that includes a plurality of surfels, wherein each surfel has associated data that includes one or more semantic labels; obtaining sensor data for one or more locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining one or more surfels corresponding to the one or more locations of the obtained sensor data; identifying one or more semantic labels for the one or more surfels corresponding to the one or more locations of the obtained sensor data; and performing, for each surfel corresponding to the one or more locations of the obtained sensor data, a label-specific detection process for the surfel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sensor calibration with environment map. In some implementations, a three-dimensional surfel representation of a real-world environment is obtained. One or more surfels of the surfel representation having a particular classification of the different classifications are selected. Input sensor data from one or more sensors installed on an autonomous or semi-autonomous vehicle are received. The input sensor data is compared to the surfel representation to identify one or more differences between the observation and the surfel representation. At least one sensor of the one or more sensors is calibrated using the one or more differences between the observation and the surfel representation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate long range localization. One of the methods includes obtaining, for a particular location of a vehicle having a camera and a detection sensor, surfel data including a plurality of surfels. Each surfel in the surfel data has a respective location and corresponds to a different respective detected surface in an environment. Image data captured by the camera is obtained. It is determined that a region of interest for detecting objects for a vehicle planning process is outside a detectable region for the detection sensor. In response, it is determined that the image data for the region of interest matches surfel color data for the surfels corresponding to the region of interest. In response, the vehicle planning process is performed with the region of interest designated as having no unexpected objects.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sensor calibration with environment map. In some implementations, a three-dimensional surfel representation of a real-world environment is obtained. One or more surfels of the surfel representation having a particular classification of the different classifications are selected. Input sensor data from one or more sensors installed on an autonomous or semi-autonomous vehicle are received. The input sensor data is compared to the surfel representation to identify one or more differences between the observation and the surfel representation. At least one sensor of the one or more sensors is calibrated using the one or more differences between the observation and the surfel representation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate long range localization. One of the methods includes obtaining, for a particular location of a vehicle having a camera and a detection sensor, surfel data including a plurality of surfels. Each surfel in the surfel data has a respective location and corresponds to a different respective detected surface in an environment. Image data captured by the camera is obtained. It is determined that a region of interest for detecting objects for a vehicle planning process is outside a detectable region for the detection sensor. In response, it is determined that the image data for the region of interest matches surfel color data for the surfels corresponding to the region of interest. In response, the vehicle planning process is performed with the region of interest designated as having no unexpected objects.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for labeling point clouds using distance field data. One of the methods includes obtaining a point cloud characterizing a region of the environment, the point cloud comprising a plurality of points; obtaining distance field data specifying, for each of a plurality of locations in the region of the environment, a distance from the location to a nearest static object in the environment; determining, using the distance field data and for each of the plurality of points, a respective distance from the point to a nearest static object in the environment to the point; and identifying, based on the respective distances for the plurality of points in the point cloud, one or more of the points as candidate dynamic object points that are likely to be measurements of a dynamic object in the environment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for the generation and use of a surfel map with semantic labels. One of the methods includes receiving a surfel map that includes a plurality of surfels, wherein each surfel has associated data that includes one or more semantic labels; obtaining sensor data for one or more locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining one or more surfels corresponding to the one or more locations of the obtained sensor data; identifying one or more semantic labels for the one or more surfels corresponding to the one or more locations of the obtained sensor data; and performing, for each surfel corresponding to the one or more locations of the obtained sensor data, a label-specific detection process for the surfel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing an initial representation of an environment to generate an updated representation that includes representations of one or more objects there were obscured in the initial representation. One of the methods includes obtaining initial surfel data comprising a plurality of surfels; determining a plurality of non-static surfels from the plurality of surfels; obtaining guidance data that characterizes a prediction for static surfaces in one or more first regions of the environment that were obscured by the plurality of non-static surfels in the initial surfel data; and processing the guidance data and the initial surfel data to generate final surfel data that comprises, for each first region of the environment, a plurality of predicted surfels that represent static surfaces in the first region that are partially or wholly obscured by objects represented by respective non-static surfels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for the generation and use of a surfel map with semantic labels. One of the methods includes receiving a surfel map that includes a plurality of surfels, wherein each surfel has associated data that includes one or more semantic labels; obtaining sensor data for one or more locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining one or more surfels corresponding to the one or more locations of the obtained sensor data; identifying one or more semantic labels for the one or more surfels corresponding to the one or more locations of the obtained sensor data; and performing, for each surfel corresponding to the one or more locations of the obtained sensor data, a label-specific detection process for the surfel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining that prediction outputs generated by a prediction system are sensitive to variations in the values of one or more of a set of target sensor calibration parameters. In one aspect, a sensitivity analysis system is configured to perform operations comprising updating the values of one or more target sensor calibration parameters of each sensor data tuple of a plurality sensor data tuples, comprising, for each sensor data tuple: providing the sensor data tuple with the current values of the target sensor calibration parameters to the prediction subsystem to generate a current prediction output; determining a gradient of a function of the current prediction output with respect to the target sensor calibration parameters; and updating the current values of the target sensor calibration parameters of the sensor data tuple using the gradient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sensor calibration with environment map. In some implementations, a three-dimensional surfel representation of a real-world environment is obtained. One or more surfels of the surfel representation having a particular classification of the different classifications are selected. Input sensor data from one or more sensors installed on an autonomous or semi-autonomous vehicle are received. The input sensor data is compared to the surfel representation to identify one or more differences between the observation and the surfel representation. At least one sensor of the one or more sensors is calibrated using the one or more differences between the observation and the surfel representation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for labeling point clouds using distance field data. One of the methods includes obtaining a point cloud characterizing a region of the environment, the point cloud comprising a plurality of points; obtaining distance field data specifying, for each of a plurality of locations in the region of the environment, a distance from the location to a nearest static object in the environment; determining, using the distance field data and for each of the plurality of points, a respective distance from the point to a nearest static object in the environment to the point; and identifying, based on the respective distances for the plurality of points in the point cloud, one or more of the points as candidate dynamic object points that are likely to be measurements of a dynamic object in the environment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using naturally collected data in sensor calibration. One of the methods includes obtaining, using a first, calibrated sensor, a first plurality of raw sensor measurements of an environment; determining, from the first plurality of raw sensor measurements, a Truncated Signed Distance Field (TSDF)-based model of surfaces in the environment; obtaining, using a second sensor, a second plurality of raw sensor measurements of the environment; determining a multi-dimensional point cloud representation of the environment; and determining refined values of the set of calibration parameters of the second sensor based on a difference between, for each data point, (i) the multiple values that define the data point and (ii) multiple values that define a target data point derived from the TSDF-based model of surfaces in the environment.

Abstract: Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling efficient object identification and tracking in autonomous vehicle (AV) applications by using velocity data-assisted mapping of first set of points obtained for a first sensing data frame by a sensing system of the AV to a second set of points obtained for a second sensing data frame by the sensing system of the AV, the first set of points and the second set of points corresponding to an object in an environment of the AV, and causing a driving path of the AV to be determined in view of the performed mapping.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate long range localization. One of the methods includes obtaining, for a particular location of a vehicle having a camera and a detection sensor, surfel data including a plurality of surfels. Each surfel in the surfel data has a respective location and corresponds to a different respective detected surface in an environment. Image data captured by the camera is obtained. It is determined that a region of interest for detecting objects for a vehicle planning process is outside a detectable region for the detection sensor. In response, it is determined that the image data for the region of interest matches surfel color data for the surfels corresponding to the region of interest. In response, the vehicle planning process is performed with the region of interest designated as having no unexpected objects.

Abstract: Methods, systems, and apparatus for generation and use of surfel maps to plan for occlusions. One of the methods includes receiving a previously-generated surfel map depicting an area in which a vehicle is located, the surfel map comprising a plurality of surfels, each surfel corresponding to a respective different location in the area in which a vehicle is located; receiving, from one or more sensors, sensor data representing the area in which the vehicle is located; determining, based on the sensor data, that the area in which a vehicle is located includes a dynamic object having a changed shape relative to its representation in the surfel map; and generating an updated path for the vehicle to travel that avoids an occlusion by the changed shape of the dynamic object of a line of sight of one or more sensors to an area of interest.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing an initial representation of an environment to generate an updated representation that includes representations of one or more objects there were obscured in the initial representation. One of the methods includes obtaining initial surfel data comprising a plurality of surfels; determining a plurality of non-static surfels from the plurality of surfels; obtaining guidance data that characterizes a prediction for static surfaces in one or more first regions of the environment that were obscured by the plurality of non-static surfels in the initial surfel data; and processing the guidance data and the initial surfel data to generate final surfel data that comprises, for each first region of the environment, a plurality of predicted surfels that represent static surfaces in the first region that are partially or wholly obscured by objects represented by respective non-static surfels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an alignment between cross-modal sensor data. In one aspect, a method comprises: obtaining (i) an image that characterizes a visual appearance of an environment, and (ii) a point cloud comprising a collection of data points that characterizes a three-dimensional geometry of the environment; processing each of a plurality of regions of the image using a visual embedding neural network to generate a respective embedding of each of the image regions; processing each of a plurality of regions of the point cloud using a shape embedding neural network to generate a respective embedding of each of the point cloud regions; and identifying a plurality of region pairs using the embeddings of the image regions and the embeddings of the point cloud regions.