Abstract: A vehicle, for example, an autonomous vehicle receives signals from a global navigation satellite system (GNSS) and determines accurate location of the vehicle using the GNSS signal. The vehicle performs localization to determine the location of the vehicle as it drives. The autonomous vehicle uses sensor data and a high definition map to determine an accurate location of the autonomous vehicle. The autonomous vehicle uses accurate location of the vehicle to determine RTK corrections that is used for improving GNSS location estimates at a future location. The RTK corrections may be transmitted to other vehicles.

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 system for generating a map is disclosed herein. The system may comprise a synthetic aperture radar (SAR) unit mountable to a terrestrial vehicle. The system may further comprise one or more computer processors operatively coupled to the SAR unit. The one or more computer processors may be individually or collectively programmed to: (i) while the terrestrial vehicle is in motion, use the SAR unit to (1) transmit a first set of signals to an environment external to the vehicle and (2) collect a second set of signals from the environment; and (ii) use at least the second set of signals to generate the map of the environment in memory.

Abstract: A vehicle, for example, an autonomous vehicle receives signals from a global navigation satellite system (GNSS) and determines accurate location of the vehicle using the GNSS signal. The vehicle performs localization to determine the location of the vehicle as it drives. The autonomous vehicle uses sensor data and a high definition map to determine an accurate location of the autonomous vehicle. The autonomous vehicle uses accurate location of the vehicle to determine RTK corrections that is used for improving GNSS location estimates at a future location. The RTK corrections may be transmitted to other vehicles.

Abstract: A vehicle, for example, an autonomous vehicle receives signals from a global navigation satellite system (GNSS) and determines accurate location of the vehicle using the GNSS signal. The vehicle performs localization to determine the location of the vehicle as it drives. The autonomous vehicle uses sensor data and a high definition map to determine an accurate location of the autonomous vehicle. The autonomous vehicle uses accurate location of the vehicle to determine RTK corrections that is used for improving GNSS location estimates at a future location. The RTK corrections may be transmitted to other vehicles.

Abstract: A vehicle, for example, an autonomous vehicle receives signals from a global navigation satellite system (GNSS) and determines accurate location of the vehicle using the GNSS signal. The vehicle performs localization to determine the location of the vehicle as it drives. The autonomous vehicle uses sensor data and a high definition map to determine an accurate location of the autonomous vehicle. The autonomous vehicle uses accurate location of the vehicle to determine RTK corrections that is used for improving GNSS location estimates at a future location. The RTK corrections may be transmitted to other vehicles.

Abstract: A vehicle, for example, an autonomous vehicle receives signals from a global navigation satellite system (GNSS) and determines accurate location of the vehicle using the GNSS signal. The vehicle performs localization to determine the location of the vehicle as it drives. The autonomous vehicle uses sensor data and a high definition map to determine an accurate location of the autonomous vehicle. The autonomous vehicle uses accurate location of the vehicle to determine RTK corrections that is used for improving GNSS location estimates at a future location. The RTK corrections may be transmitted to other vehicles.

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 vehicle, for example, an autonomous vehicle receives signals from a global navigation satellite system (GNSS) and determines accurate location of the vehicle using the GNSS signal. The vehicle performs localization to determine the location of the vehicle as it drives. The autonomous vehicle uses sensor data and a high definition map to determine an accurate location of the autonomous vehicle. The autonomous vehicle uses accurate location of the vehicle to determine RTK corrections that is used for improving GNSS location estimates at a future location. The RTK corrections may be transmitted to other vehicles.

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 sub-graphs 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 computer model of a physical structure (or object) can be generated using context-based hypothesis testing. For a set of point data, a user selects a context specifying a geometric category corresponding to the structure shape. The user specifies at least one seed point from the set that lies on a surface of the structure of interest. Using the context and point data, the system loads points in a region near the seed point(s), and determines the dimensions and orientation of an initial surface component in the context that corresponds to those points. If the selected component is supported by the points, that component can be added to a computer model of the surface. The system can repeatedly find points near a possible extension of the surface model, using the context and current surface component(s) to generate hypotheses for extending the surface model to these points.

Abstract: A computer model of a physical structure (or object) can be generated using context-based hypothesis testing. For a set of point data, a user selects a context specifying a geometric category corresponding to the structure shape. The user specifies at least one seed point from the set that lies on a surface of the structure of interest. Using the context and point data, the system loads points in a region near the seed point(s), and determines the dimensions and orientation of an initial surface component in the context that corresponds to those points. If the selected component is supported by the points, that component can be added to a computer model of the surface. The system can repeatedly find points near a possible extension of the surface model, using the context and current surface component(s) to generate hypotheses for extending the surface model to these points.

Abstract: A computer model of a physical structure (or object) can be generated using context-based hypothesis testing. For a set of point data, a user selects a context specifying a geometric category corresponding to the structure shape. The user specifies at least one seed point from the set that lies on a surface of the structure of interest. Using the context and point data, the system loads points in a region near the seed point(s), and determines the dimensions and orientation of an initial surface component in the context that corresponds to those points. If the selected component is supported by the points, that component can be added to a computer model of the surface. The system can repeatedly find points near a possible extension of the surface model, using the context and current surface component(s) to generate hypotheses for extending the surface model to these points.

Abstract: A polypropylene resin composition for molding material, which is excellent in an appearance of weld line and an appearance of tiger stripe and is used for automobile exterior parts and an automobile exterior part comprising the same, are provided.

Abstract: In a DPP-type tracking error signal (TE signal), an offset caused by the difference between the reflectance of a main-beam irradiated part and the reflectance of side-beam irradiated parts is appropriately compensated without individual normalization of an MPP signal and an SPP signal. An analog signal processing unit 40 produces the MPP, MPI, SPP, and SPI signals on the basis of the amounts of received reflected light of the main beam and side beams detected by a photo-detection unit 130, produces a DPP-type TE signal and a CE signal on the basis of the MPP and/or SPP signal, and outputs the analog signals of the TE, CE, MPI, and SPI signals.

Abstract: The present invention provides a resin composition suitable for molding a microscopic structure that has small cure shrinkage, good mold transfer properties and excellent properties for post-processing such as polishing processing and laser processing. Such a resin is a thermosetting resin composition comprising 95 to 35 wt % of a thermosetting resin and 5 to 65 wt % of organic filler having a particle size of 10 ?m or less. From such a resin, an ink ejecting apparatus having a microscopic structure, for example, a nozzle of a diameter of 30 ?m, is integrally molded.

Abstract: A method and a device for discharging a liquid specimen capable of controlling the sizes of substances in the liquid specimen. The device includes a cylinder having a discharge port for a liquid specimen and a supply port for the liquid specimen and discharging means for discharging the liquid specimen, and a means for generating a non-uniform electric field in an internal space of the cylinder is provided in the cylinder. Using this device for discharging a liquid specimen, the sizes of the target substances in the liquid specimen to be discharged can be controlled by discharging the liquid specimen while generating an appropriate non-uniform electric field in the internal space of the cylinder.

Abstract: The present invention provides a method that allows ultra-precisely bonding by which a complete sealingly bonded molded article can be produced in a simple manner. This method includes (1) fixing an injection molded article (a) at a position (predetermined bonding position) formed with a mold; (2) mounting and moving a molded article (b) to be bonded to the injection molded article (a) to the predetermined bonding position within the mold; (3) positioning the molded article (b) at the predetermined bonding position; and (4) closing the mold for bonding.

Abstract: The present invention provides a resin composition suitable for molding a microscopic structure that has small cure shrinkage, good mold transfer properties and excellent properties for post-processing such as polishing processing and laser processing. Such a resin is a thermosetting resin composition comprising 95 to 35 wt % of a thermosetting resin and 5 to 65 wt % of organic filler having a particle size of 10 &mgr;m or less. From such a resin, an ink ejecting apparatus having a microscopic structure, for example, a nozzle of a diameter of 30 &mgr;m, is integrally molded.