Methods and systems for camera to ground alignment
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.
1. A method for a vehicle, the method comprising:
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.
2. The method of claim 1 , wherein the three dimensional projection method determines a feature point from a two dimensional image sequence from the plurality of images using back-projection of the feature point from a two dimensional image to a unit plane.
3. The method of claim 2 , wherein the three dimensional projection method further determines the feature point from the two dimensional image sequence by determining feature shifting in the unit plane based on the vehicle data, and predicting a three dimensional point of the feature point based on the feature shifting.
4. The method of claim 3 , wherein the three dimensional projection method further determines the feature point by projecting the three dimensional point of the feature point back to the two dimensional image based on the feature shifting.
5. The method of claim 1 , wherein the selecting the subset of feature points is based on a fixed two dimensional image road mask and a three dimensional region.
6. The method of claim 1 , wherein the selecting the subset of feature points is based on a dynamic two dimensional image road mask.
7. The method of claim 1 , wherein the selecting the subset of feature points is based on homography constraints.
8. The method of claim 1 , further comprising:
receiving lighting data indicating lighting conditions associated with the environment of the vehicle;
selecting at least one region of interest within the at least one image based on the lighting data; and
wherein the determining the feature points is based on the at least one region of interest.
9. The method of claim 7 , wherein the lighting data includes at least one of environmental lighting data, climate data, and time of day data.
10. The method of claim 1 , further comprising:
evaluating vehicle conditions to determine when the vehicle driving is smooth and straight; and
wherein in response to the determination that the vehicle is driving smooth and straight, the determining the feature points is performed.
11. The method of claim 10 , wherein the vehicle conditions include vehicle acceleration, vehicle velocity, and steering wheel angle.
12. The method of claim 10 , wherein the vehicle conditions include a determined distance travelled between two images of the plurality of images.
13. The method of claim 1 , further comprising:
refining the ground normal vector based on a sliding window method associated with the plurality of images.
14. The method of claim 1 , wherein the refining the ground normal vector is further based on a minimization of a computed transfer distance based on homography.
15. The method of claim 1 , wherein the determining the ground plane is based on constraints associated with a reference ground normal vector and a motion direction.
16. A computer implemented system for a vehicle, the system comprising:
a data storage element comprising computer readable instructions; and
a processor configured to execute the computer readable instructions, the computer readable instructions controlling the processor to perform operations comprising:
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 feature points within at least one image based on the vehicle data and a three-dimensional projection method;
selecting a subset of the feature points as ground points;
determining a ground plane based on the subset of feature points;
determining a ground normal vector from the ground plane;
determining a camera to ground alignment value based on the ground normal vector; and
generating second image data based on the camera to ground alignment value.
17. The system of claim 16 , wherein the three dimensional projection method determines a feature point from a two dimensional image sequence from the plurality of images using back-projection of the feature point from a two dimensional image to a unit plane.
18. The system of claim 17 , wherein the three dimensional projection method further determines the feature point from the two dimensional image sequence by determining feature shifting in the unit plane based on the vehicle data, predicting a three dimensional point of the feature point based on the feature shifting, and projecting the three dimensional feature point of the feature point back to the two dimensional image based on the feature shifting.
19. A vehicle, comprising:
a camera configured to capture images of an environment of the vehicle; and
a controller having a processor configured to receive the images, receive vehicle data indicating a velocity of the vehicle, wherein the vehicle data is associated with the images, determine feature points within at least one image based on the vehicle data and a three-dimensional projection method, select a subset of the feature points as ground points, determine a ground plane based on the subset of feature points, determine a ground normal vector from the ground plane, determine a camera to ground alignment value based on the ground normal vector.
20. The vehicle of claim 19 , wherein the processor is configured select the subset of feature points based on a fixed two dimensional image road mask and a three dimensional region.