Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: The technology provides enhanced localization approaches using vehicle-obtained information in place of or to enhance global positioning information to localize a user's position. Such information can be shared with the user's client device in real-time prior to pickup or meeting at a selected location. This can supplement or replace inaccurate localization information available at the client device, and can be done as needed when the user is within a threshold range of one or more autonomous vehicles. A client device of a user can compute its position using the vehicle positioning information. The vehicle positioning approach may be performed when the user is within a certain range of one or more vehicles. Vehicle localization information may also be used to correct the client device's localization information. Here, using hyper-accurate vehicle positioning, one or more vehicles can compute pseudorange errors for each “visible” satellite in a global positioning service.

Abstract: The disclosure provides for a method of calibrating a detection system that is mounted on a vehicle. The method includes detecting characteristics of the mirror and characteristics of a vehicle portion using the detection system. The mirror reflects the vehicle portion to be detected using the detection system. The method also includes determining a first transform based on the detected one or more of mirror characteristics, determining a second transform based on the one or more vehicle portion characteristics, and determining a third transform based on a known position of the vehicle portion in relation to the vehicle. Further, the method includes determining a position of the detection system relative to the vehicle based on the first, second, and third transforms and then calibrating the detection system using the determined position of the detection system relative to the vehicle.

Abstract: The disclosure provides for a method of calibrating a detection system that is mounted on a vehicle. The method includes detecting characteristics of the mirror and characteristics of a vehicle portion using the detection system. The mirror reflects the vehicle portion to be detected using the detection system. The method also includes determining a first transform based on the detected one or more of mirror characteristics, determining a second transform based on the one or more vehicle portion characteristics, and determining a third transform based on a known position of the vehicle portion in relation to the vehicle. Further, the method includes determining a position of the detection system relative to the vehicle based on the first, second, and third transforms and then calibrating the detection system using the determined position of the detection system relative to the vehicle.

Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: The disclosure provides for a method of calibrating a detection system that is mounted on a vehicle. The method includes detecting characteristics of the mirror and characteristics of a vehicle portion using the detection system. The mirror reflects the vehicle portion to be detected using the detection system. The method also includes determining a first transform based on the detected one or more of mirror characteristics, determining a second transform based on the one or more vehicle portion characteristics, and determining a third transform based on a known position of the vehicle portion in relation to the vehicle. Further, the method includes determining a position of the detection system relative to the vehicle based on the first, second, and third transforms and then calibrating the detection system using the determined position of the detection system relative to the vehicle.

Abstract: Aspects of the disclosure provide for a method of calibrating a vehicle's plurality of detection systems. To calibrate a first detection system, orientation data is collected using the first detection device when the vehicle faces a first direction and a second direction opposite the first direction. To calibrate a second detection system, data is collected using the second detection device while the vehicle moves in a repeatable pattern near a first object. To calibrate a third detection system, light reflected off a second object is detected using the third detection system as the vehicle is moved towards the second object. To calibrate a fourth detection system, data collected using the second detection system and the fourth detection system is compared. To calibrate a fifth detection system, radar signals reflected off a third object is received using the fifth detection system while the vehicle moves relative to the third object.

Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: Camera pose optimization, which includes determining the position and orientation of a camera in three-dimensional space at different times, is improved by detecting a higher-confidence reference object in the photographs captured by the camera and using the object to increase consistency and accuracy of pose data. Higher-confidence reference objects include objects that are stationary, fixed, easily recognized, and relatively large. In one embodiment, street level photographs of a geographic area are collected by a vehicle with a camera. The captured images are geo-coded using GPS data, which may be inaccurate. The vehicle drives in a loop and captures the same reference object multiple times from the substantially same position. The trajectory of the vehicle is then closed by aligning the points of multiple images where the trajectory crosses itself. This creates an additional constraint on the pose data, which in turn improves the data's consistency and accuracy.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: Aspects of the disclosure relate to detecting vehicle movement. For example, one or more computing devices may receive first image data representative of a vehicle's wheel and second image data representative of the wheel captured subsequent to the capture of the first image data. The one or more computing devices may determine a first location of a first portion of the wheel based on the first image data, and a second location of the first portion of the wheel based on the second image data. The one or more computing devices may calculate a value based on the angular distance between the first location and the second location of the first portion, and based on the value, determine whether the vehicle is in motion. Upon determining the vehicle is in motion the one or more computing devices may provide a signal that the vehicle is in motion.

Abstract: Camera pose optimization, which includes determining the position and orientation of a camera in three-dimensional space at different times, is improved by detecting a higher-confidence reference object in the photographs captured by the camera and using the object to increase consistency and accuracy of pose data. Higher-confidence reference objects include objects that are stationary, fixed, easily recognized, and relatively large. In one embodiment, street level photographs of a geographic area are collected by a vehicle with a camera. The captured images are geo-coded using GPS data, which may be inaccurate. The vehicle drives in a loop and captures the same reference object multiple times from the substantially same position. The trajectory of the vehicle is then closed by aligning the points of multiple images where the trajectory crosses itself. This creates an additional constraint on the pose data, which in turn improves the data's consistency and accuracy.

Abstract: Aspects of the disclosure relate to detecting vehicle movement. For example, one or more computing devices may receive first image data representative of a vehicle's wheel and second image data representative of the wheel captured subsequent to the capture of the first image data. The one or more computing devices may determine a first location of a first portion of the wheel based on the first image data, and a second location of the first portion of the wheel based on the second image data. The one or more computing devices may calculate a value based on the angular distance between the first location and the second location of the first portion, and based on the value, determine whether the vehicle is in motion. Upon determining the vehicle is in motion the one or more computing devices may provide a signal that the vehicle is in motion.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: Camera pose optimization, which includes determining the position and orientation of a camera in three-dimensional space at different times, is improved by detecting a higher-confidence reference object in the photographs captured by the camera and using the object to increase consistency and accuracy of pose data. Higher-confidence reference objects include objects that are stationary, fixed, easily recognized, and relatively large. In one embodiment, street level photographs of a geographic area are collected by a vehicle with a camera. The captured images are geo-coded using GPS data, which may be inaccurate. The vehicle drives in a loop and captures the same reference object multiple times from the substantially same position. The trajectory of the vehicle is then closed by aligning the points of multiple images where the trajectory crosses itself. This creates an additional constraint on the pose data, which in turn improves the data's consistency and accuracy.

Abstract: Aspects of the disclosure relate to detecting vehicle movement. For example, one or more computing devices may receive first image data representative of a vehicle's wheel and second image data representative of the wheel captured subsequent to the capture of the first image data. The one or more computing devices may determine a first location of a first portion of the wheel based on the first image data, and a second location of the first portion of the wheel based on the second image data. The one or more computing devices may calculate a value based on the angular distance between the first location and the second location of the first portion, and based on the value, determine whether the vehicle is in motion. Upon determining the vehicle is in motion the one or more computing devices may provide a signal that the vehicle is in motion.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: Systems and methods for providing a visualization of satellite sightline obstructions are provided. An example method includes identifying an approximate position of a receiver antenna. The method further includes providing a rendering of a physical environment surrounding the receiver antenna for display within a user interface. The user interface can be provided on a display. Satellite positional data associated with the position of a satellite is accessed and a sightline between the approximate position of the receiver antenna and the position of the satellite is determined. The method further includes presenting the sightline within the user interface in association with the rendering. An example system includes a data capture system and a computing device to provide a visualization of satellite sightline obstructions.