Abstract: Methods and apparatus consistent with the present disclosure may use a plurality of force sensing devices that allow a computer of an autonomous vehicle (AV) to identify locations within the AV where particular objects reside. These force sensing devices may allow the AV computer to identify how much a particular object weights and identify whether an object has moved based on a location where a particular amount of weight currently resides versus a location where that weight previously resided in the AV. Weight information combined with other sensor data (e.g. camera or LIDAR data) may allow the AV computer to identify whether a particular object is a person or a package. The AV computer may also validate that certain driving rules are enforced, such that persons or packages are being moved safely and correctly in the AV.

Abstract: A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

Abstract: A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

Abstract: A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

Abstract: Apparatus and methods are provided for using a self-illuminating harp to calibrate for distortion. In particular, apparatus, methods and systems are provided for imaging and analyzing self-illuminated harp in order to correct for lens distortion. In various implementations, an array of media is strung plumb which resembles a harp. The media can be light fibers or other suitable material which can be strung in a straight line. The cores of translucent fibers are illuminated. These are configured to scatter light throughout their bulk which escapes the core by egressing along the length of fiber. This light produces plumb lines which can be imaged, analyzed and model. In some implementations, imaging is performed by rotation to include a full FOV.

Abstract: A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

Abstract: To calibrate a set of sensors with respect to one another, such as the respective position and orientation of the sensors, a sensor calibration system uses a well-measured calibration scene in which the objects within the calibration scene have established positions, and optionally orientations (e.g., rotational directions), within the calibration scene and with respect to one another. The calibration scene is captured by a set of sensors to generate respective sensor views of the scene. Each sensor view is analyzed to detect control objects (e.g., features thereof) with respect to the coordinates of each sensor view. Using the known relationship of the calibration objects within the calibration scene, the sensors are calibrated to determine calibration parameters for a joint coordinate system that the maps the detected positions in the sensor view with the relative positions of the measured calibration scene.

Abstract: A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

Abstract: A computer-implemented method includes receiving, by a computing system, a first image of an optical target from a camera. The first image is associated with a first position of the camera. The method also includes receiving, by the computing system, a second image of the optical target, Wherein the second image is associated with a second position of the camera. The method also includes determining an estimated displacement between the first position of the camera and the second position of the camera based on the first image and the second image. The method further includes determining a focal length based on the estimated displacement and a measured displacement of the camera between the first position and the second position.

Abstract: A computer-implemented method for focal length validation may include receiving, by a computing system, initial image data for an optical target from a camera. The method may also include determining a distortion parameter associated with the camera, based on the initial image data. The method may also include receiving, by the computing system, a first image of the optical target, wherein the first image is associated with a first position of the camera. The method may also include receiving, by the computing system, a second image of the optical target, wherein the second image is associated with a second position of the camera. The method may also include determining an estimated displacement between the first position of the camera and the second position of the camera based on the first image and the second image. The method may further include determining a focal length based on the estimated displacement and a measured displacement of the camera between the first position and the second position.