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公开(公告)号：US12131550B1
公开(公告)日：2024-10-29
申请号：US17138125
申请日：2020-12-30
Applicant: Waymo LLC
Inventor： Colin Braley , Volodymyr Ivanchenko
IPC: G06V20/58 , B60W60/00 , G06F18/213 , G06V10/75
CPC classification number: G06V20/58 , B60W60/001 , G06F18/213 , G06V10/751 , B60W2420/403 , B60W2420/408 , B60W2554/20
Abstract: In one example, a method is provided that includes receiving lidar data obtained by a lidar device. The lidar data includes a plurality of data points indicative of locations of reflections from an environment of the vehicle. The method includes receiving images of portions of the environment captured by a camera at different times. The method also includes determining locations in the images that correspond to a data point of the plurality of data points. Additionally, the method includes determining feature descriptors for the locations of the images and comparing the feature descriptors to determine that sensor data associated with at least one of the lidar device, the camera, or a pose sensor is accurate or inaccurate.
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公开(公告)号：US11945467B2
公开(公告)日：2024-04-02
申请号：US18193199
申请日：2023-03-30
Applicant: Waymo LLC
Inventor： Volker Grabe , Colin Braley , Volodymyr Ivanchenko , Alexander Meade
CPC classification number: B60W60/001 , B62D15/021 , G01C21/3415 , G06V20/588 , G08G1/20 , H04W4/46 , B60W2420/52 , B60W2554/4049
Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.
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公开(公告)号：US11765067B1
公开(公告)日：2023-09-19
申请号：US17131388
申请日：2020-12-22
Applicant: Waymo LLC
Inventor： Volodymyr Ivanchenko , Volker Grabe
CPC classification number: H04L43/50 , B60W50/0205 , B60W50/045 , B60W50/14 , B60W60/001 , B60W60/005 , H04L43/065 , H04L67/12 , B60W2050/146 , B60W2420/42 , B60W2420/52
Abstract: A method includes monitoring, at a computing device, outputs of a sensor validator. Each output is generated by the sensor validator based on corresponding sensor data from a sensor coupled to an autonomous vehicle, and each output indicates whether the corresponding sensor data is associated with an event. The method also includes mutating, at the computing device, particular sensor data to generate mutated sensor data that is associated with a particular event. The method further includes determining, at the computing device, a performance metric associated with the sensor validator based on a particular output generated by the sensor validator. The particular output is based on the mutated sensor data.
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公开(公告)号：US12085678B1
公开(公告)日：2024-09-10
申请号：US17138180
申请日：2020-12-30
Applicant: Waymo LLC
Inventor： Gil Shotan , Colin Braley , Volker Grabe , Volodymyr Ivanchenko
CPC classification number: G01S7/497 , G01S7/4817 , G01S17/894 , G05D1/0291 , G08G1/22 , B60W50/06 , B60W60/001 , B60W2420/403 , B60W2420/408
Abstract: Example embodiments relate to methods for localizing light detection and ranging (lidar) calibration targets. An example method includes generating a point cloud of a region based on data from a light detection and ranging (lidar) device. The point cloud may include points representing at least a portion of a calibration target. The method also includes determining a presumed location of the calibration target. Further, the method includes identifying, within the point cloud, a location of a first edge of the calibration target. In addition, the method includes performing a comparison between the identified location of the first edge of the calibration target and a hypothetical location of the first edge of the calibration target within the point cloud if the calibration target were positioned at the presumed location. Still further, the method includes revising the presumed location of the calibration target based on at least the comparison.
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公开(公告)号：US20240393443A1
公开(公告)日：2024-11-28
申请号：US18794511
申请日：2024-08-05
Applicant: Waymo LLC
Inventor： Gil Shotan , Colin Braley , Volker Grabe , Volodymyr Ivanchenko
Abstract: Example embodiments relate to methods for localizing light detection and ranging (lidar) calibration targets. An example method includes generating a point cloud of a region based on data from a light detection and ranging (lidar) device. The point cloud may include points representing at least a portion of a calibration target. The method also includes determining a presumed location of the calibration target. Further, the method includes identifying, within the point cloud, a location of a first edge of the calibration target. In addition, the method includes performing a comparison between the identified location of the first edge of the calibration target and a hypothetical location of the first edge of the calibration target within the point cloud if the calibration target were positioned at the presumed location. Still further, the method includes revising the presumed location of the calibration target based on at least the comparison.
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Patent Agency Ranking
公开(公告)号：US20240182065A1
公开(公告)日：2024-06-06
申请号：US18437476
申请日：2024-02-09
Applicant: Waymo LLC
Inventor： Volker Grabe , Colin Braley , Volodymyr Ivanchenko , Alexander Meade
CPC classification number: B60W60/001 , B62D15/021 , G01C21/3415 , G06V20/588 , G08G1/20 , H04W4/46 , B60W2420/408 , B60W2554/4049
Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.
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公开(公告)号：US20220076082A1
公开(公告)日：2022-03-10
申请号：US17531444
申请日：2021-11-19
Applicant: Waymo LLC
Inventor： Colin Andrew Braley , Volodymyr Ivanchenko , Yu Zhang
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.
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公开(公告)号：US11195064B2
公开(公告)日：2021-12-07
申请号：US16509152
申请日：2019-07-11
Applicant: Waymo LLC
Inventor： Colin Andrew Braley , Volodymyr Ivanchenko , Yu Zhang
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.
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公开(公告)号：US20210197854A1
公开(公告)日：2021-07-01
申请号：US17138083
申请日：2020-12-30
Applicant: Waymo LLC
Inventor： Volker Grabe , Colin Braley , Volodymyr Ivanchenko , Alexander Meade
Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.
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公开(公告)号：US20210012166A1
公开(公告)日：2021-01-14
申请号：US16509152
申请日：2019-07-11
Applicant: Waymo LLC
Inventor： Colin Andrew Braley , Volodymyr Ivanchenko , Yu Zhang
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.
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