Abstract: Method for sensor alignment including detecting a depth point cloud including a first object and a second object, generating a first control point in response to a location of the first object within the depth point cloud and a second control point in response to a location of the second object within the depth point cloud, capturing an image of a second field of view including a third object, generating a third control point in response to a location of the third object detected in response to the image, calculating a first reprojection error in response to the first control point and the third control point and a second reprojection error in response to the second control point and the third control point, generating an extrinsic parameter in response to the first reprojection error in response to the first reprojection error being less than the second reprojection error.

Abstract: A sensor alignment method including detecting, detecting an object within a first sensor field of view and a second sensor field of view, detecting a plurality of depth point clouds of the first field of view in response to the object being stationary, the host vehicle being stationary, and the distance between the object and the host vehicle being less than a threshold distance, aggregating the plurality of depth point clouds into an aggregated depth point cloud, detecting a first location of an edge of the object in response to the aggregated depth point cloud, detecting a second location of the edge of the object using an edge detection algorithm on the image, generating a lidar to camera alignment in response to a difference between the first location and the second location.

Abstract: Systems and methods are provided for generating alignment parameters for processing data associated with a vehicle. In one embodiment, a method includes: receiving image data associated with an environment of the vehicle; receiving lidar data associated with the environment of the vehicle; processing, by a processor, the image data to determine data points associated with at least one pole identified within image data; processing, by the processor, the lidar data to determine data points associated with at least one pole identified within the lidar data; selectively storing the data points as data point pairs in a data buffer; iteratively processing, by the processor, the data point pairs with a plurality of perturbations to determine a transformation matrix; generating, by the processor, alignment data based on the transformation matrix; and processing future data based on the alignment parameters.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by a controller onboard the vehicle, lidar data from the lidar device; receiving, by the controller, image data from the camera device; computing, by the controller, an edge map based on the lidar data; computing, by the controller, an inverse distance transformation (IDT) edge map based on the image data; aligning, by the controller, points of the IDT edge map with points of the lidar edge map to determine extrinsic parameters; storing, by the controller, extrinsic parameters as calibrations in a data storage device; and controlling, by the controller, the vehicle based on the stored calibrations.

Abstract: Systems and methods for generating alignment parameters for processing data associated with a vehicle. In one embodiment, a method includes: receiving image data associated with an environment of the vehicle; receiving lidar data associated with the environment of the vehicle; processing, by a processor, the image data to determine data points associated with at least one vehicle identified within image data; processing, by the processor, the lidar data to determine data points associated with at least one vehicle identified within the lidar data; selectively storing the data points in a data buffer based on at least one condition associated with a quality of the data points; processing, by the processor, the data points in the data buffer with a joint analysis method to generate alignment parameters between the lidar and the camera; and processing future data based on the alignment parameters.

Abstract: Systems and methods are provided for generating alignment parameters for processing data associated with a vehicle. In one embodiment, a method includes: receiving image data associated with an environment of the vehicle; receiving lidar data associated with the environment of the vehicle; processing, by a processor, the image data to determine data points associated with at least one pole identified within image data; processing, by the processor, the lidar data to determine data points associated with at least one pole identified within the lidar data; selectively storing the data points as data point pairs in a data buffer; iteratively processing, by the processor, the data point pairs with a plurality of perturbations to determine a transformation matrix; generating, by the processor, alignment data based on the transformation matrix; and processing future data based on the alignment parameters.

Abstract: Systems and methods for generating alignment parameters for processing data associated with a vehicle. In one embodiment, a method includes: receiving image data associated with an environment of the vehicle; receiving lidar data associated with the environment of the vehicle; processing, by a processor, the image data to determine data points associated with at least one vehicle identified within image data; processing, by the processor, the lidar data to determine data points associated with at least one vehicle identified within the lidar data; selectively storing the data points in a data buffer based on at least one condition associated with a quality of the data points; processing, by the processor, the data points in the data buffer with a joint analysis method to generate alignment parameters between the lidar and the camera; and processing future data based on the alignment parameters.

Abstract: A camera of a vehicle has a field of view. A perception module of the vehicle is configured to determine a location of an object relative to the vehicle based on a model for the camera and a camera ground truth orientation of the camera. A calibration module is configured to: wirelessly receive images from the camera; determine a vehicle camera target based on the images; when the vehicle camera target is aligned with a calibration module target on the calibration module, determine a present orientation of the calibration module measured using a gyroscope of the calibration module; when at least one component of the present orientation of the calibration module is different than the at least one component of an expected orientation of the calibration module, determine an updated camera ground truth orientation of the camera; and wirelessly transmit the updated ground truth orientation to the vehicle.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by a controller onboard the vehicle, lidar data from the lidar device; receiving, by the controller, image data from the camera device; computing, by the controller, an edge map based on the lidar data; computing, by the controller, an inverse distance transformation (IDT) edge map based on the image data; aligning, by the controller, points of the IDT edge map with points of the lidar edge map to determine extrinsic parameters; storing, by the controller, extrinsic parameters as calibrations in a data storage device; and controlling, by the controller, the vehicle based on the stored calibrations.