Gm Global Technolgoy Operations, Inc.

Binbin Li

Xinyu Du

Yao Hu

Hao Yu

Wende Zhang

Guanlun He

A computer-implemented method for aligning a sensor to reference coordinate system includes initiating a plurality of threads, each thread executes simultaneously and independent of each other. A first thread parses data received from the sensor and stores the parsed data in a data buffer. A second thread computes an alignment transformation using the parsed data to determine alignment between the sensor and the reference coordinate system. The computing includes checking that the data buffer contains at least predetermined amount of data. If at least the predetermined amount of data exists, an intermediate result is computed using the parsed data in the data buffer; otherwise, the second thread waits for the first thread to add more data to the data buffer. The second thread outputs the intermediate result into the data buffer. A third thread outputs the alignment transformation, in response to completion of alignment computations.

Vehicle-onboard computing architecture for sensor alignment

A vehicle includes a system performing a method of operating a camera for the vehicle. The camera is configured to capture an image. The processor is configured to query the camera to obtain an intrinsic parameter of the camera, estimate a unified parameter based on the intrinsic parameter of the camera, and perform at least one application on the image using the unified parameter.

Automatic unification of camera intrinsic model

A vehicle includes a system that performs a method for aligning a first camera and a second camera to a reference frame of the vehicle. The first camera has a first coordinate system, and the second camera has a second coordinate system. The first camera obtains a first image. The processor is configured to obtain a first camera-to-ground transformation matrix for the first camera using the first image, wherein the first camera-to-ground transformation matrix relates the first coordinate system to a ground coordinate system, obtain a camera-to-camera transformation matrix between the first coordinate system of the first camera and the second coordinate system of the second camera, calculate a constructed camera-to-ground transformation matrix for the second camera using the first camera-to-ground transformation matrix and the camera-to-camera transformation matrix, and update the second coordinate system of the second camera using the constructed camera-to-ground transformation.

Camera to vehicle alignment with multiple camera adjustment

A system includes a platform operational to move through an environment, a navigation system operational to measure platform poses of the platform in the environment, a camera operational to generate images of the environment at multiple timestamps, and a computer. The computer is operational to receive a first image from the camera, receive a second image from the camera, estimate a view direction of the first camera pose based on a first and second platform poses and alignment parameters, generate a rendered portion of the environment with a neural radiance field technique based on the second image and the alignment parameters, generate a predicted image of the environment as observed along the view direction through the rendered portion of the environment, determine differences between the first image and the predicted image, and update one or more of the plurality of alignment parameters based on the one or more differences.

Neural radiance field based camera alignment

A camera misalignment detection system detecting a misalignment condition of one or more cameras for a vehicle includes one or more controllers that determine a set of matched pixel pairs, determine a feature matching ratio based on the set of matched pixel pairs, and calculate an alignment angle difference of the one or more cameras. In response to determining the feature matching ratio, the essential matrix inlier ratio, and the alignment angle difference each exceed respective threshold values, the controllers add the alignment angle difference to a queue including a sequence of historical alignment angle difference values. The controllers perform statistical filtering to determine a total number of historical alignment angle difference values within the queue that are inliers and determine a misalignment condition of the one or more cameras based on the total number of historical alignment angle difference values within the queue that are inliers.

Camera misalignment detection system for a vehicle

A vehicle, a system and a method of navigating the vehicle. The system includes a camera conveyed by the vehicle and a processor. The camera is configured to obtain an original image data file of an environment. The processor is configured to reduce the original image data file to obtain a reduced image data file and determine an alignment between a camera-centered coordinate system and a ground coordinate system using the reduced image data file.

System and method for online camera to ground alignment

A system for on-vehicle camera alignment monitoring includes an on-vehicle camera in communication with a controller. The controller monitors vehicle operating parameters and camera signal parameters, and captures an image file from the on-vehicle camera. A first level analysis of the image file, the vehicle operating parameters, and the camera signal parameters is executed to detect dynamic conditions and image feature parameters that affect camera alignment. An error with one of the dynamic conditions or the image feature parameters that affects the camera alignment is detected. A second level analysis of the camera signal parameters is executed to identify a root cause indicating one of the dynamic conditions or the image feature parameters that affects the camera alignment based upon the error. A camera alignment-related fault is detected based upon the root cause, and vehicle operation is controlled based upon the camera alignment-related fault.

On-vehicle camera alignment monitoring system

Systems and methods for a vehicle are provided. In one embodiment, a method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; determining, by a processor, feature points within at least one image of the plurality of images; selecting, by the processor, a subset of the feature points as ground points based on a fixed two dimensional image road mask and a three dimensional region; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

Methods and systems for camera to ground alignment

Methods and systems for a vehicle are provided. In one embodiment, the method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; determining, by a processor, feature points within at least one image of the plurality of images; selecting, by the processor, a subset of the feature points as ground points; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, the ground normal vector based on a sliding widow method; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

A vehicle control system including a spatial monitoring system includes on-vehicle cameras that capture images, from which are recovered a plurality of three-dimensional points. A left ground plane normal vector is determined for a left image, a center ground plane normal vector is determined for a front image, and a right ground plane normal vector is determined for a right image. A first angle difference between the left ground plane normal vector and the center ground plane normal vector is determined, and a second angle difference between the right ground plane normal vector and the center ground plane normal vector is determined. An uneven ground surface is determined based upon one of the first angle difference or the second angle difference, and an alignment compensation factor for the left camera or the right camera is determined. A bird's eye view image is determined based upon the alignment compensation factor.

On-vehicle spatial monitoring system

Systems and methods for a vehicle are provided. In one embodiment, a method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; receiving vehicle data indicating a velocity of the vehicle, wherein the vehicle data is associated with the image data; determining, by a processor, feature points within at least one image based on the vehicle data and a three dimensional projection method; selecting, by the processor, a subset of the feature points as ground points; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

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College Station, TX, United States of America

Binbin Li