Method of detecting obstacle around vehicle
Disclosed is a method of detecting obstacle around a vehicle. The method of detecting an obstacle around a vehicle, includes: acquiring an image of the obstacle around the vehicle using a monocular camera; creating, by a controller, a distance based cost map, a color based cost map and an edge based cost map from the image; and integrating, by the controller, the distance based cost map, the color based cost map, and the edge based cost map to create a final cost map, and estimating, by the controller, a height of the obstacle from the final cost map.
1. A method of detecting an obstacle around a vehicle, comprising:
acquiring an image of the obstacle around the vehicle using a monocular camera;
creating, by a controller, a distance based cost map, a color based cost map and an edge based cost map from the image; and
integrating, by the controller, the distance based cost map, the color based cost map, and the edge based cost map to generate a final cost map, and estimating by the controller a height of the obstacle from the final cost map.
2. The method of claim 1 , wherein the creating of the distance based cost map comprises:
generating a plurality of Delaunay triangles by connecting feature points in the image and performing interpolation of distance information using a plane equation generated by three vertexes of each of the plurality of the Delaunay triangles; and
estimating a disparity of pixels included in each of the plurality of the Delaunay triangles from a result of the interpolation.
3. The method of claim 1 , wherein the creating of the color based cost map comprises:
setting an area where color similarities are measured in the image, measuring the color similarities between all feature points existing in the area, and selecting a greatest color similarity value as a final color similarity; and
calculating a difference between sums of the color similarities from the measured color similarities to create the color based cost map.
4. The method of claim 1 , wherein the creating of the edge based cost map comprises:
performing edge detection on the image; and
performing distance transformation based on edge detection such that pixels located closer to an edge have lower values.
5. The method of claim 1 , wherein the final cost map is calculated as:
c t =w d c d +w c c c +w e c e ,
where: w d is a weight of the distance based cost map, w c is a weight of the color based cost map, w e is a weight of the edge based cost map, c d is the distance based cost map, c c is the color based cost map, and c e is the edge based cost map.
6. A method of detecting an obstacle around a vehicle, comprising:
acquiring an image of the obstacle around the vehicle using a monocular camera;
reconstructing, by a controller, three-dimensional positions of corresponding points in the image;
integrating, by the controller, previously reconstructed three-dimensional corresponding points with the currently reconstructed three-dimensional corresponding points based on a relative positional relationship between the previously reconstructed three-dimensional corresponding points and the currently reconstructed three-dimensional corresponding points;
calculating, by the controller, a disparity value by applying a virtual baseline value formed by a movement of the monocular camera to a depth value obtained through three-dimensional reconstruction of the corresponding points; and
estimating, by the controller, a boundary of the obstacle based on the disparity value.
7. The method of claim 6 , wherein the disparity value is calculated as:
d
=
f
B
Z
,
where, the d is the disparity value to be obtained, the B is the virtual baseline value of the monocular camera, the Z is the depth value obtained through three-dimensional reconstruction, and the f is a focal length of the monocular camera.
8. The method of claim 6 , further comprises changing, when an angle of view of the monocular camera is a wide angle that is greater than or equal to a predetermined angle of view, a u-axis to an incident angular axis θu through the following equation:
θ
u
=
atan
(
u
-
o
x
f
)
,
where, the u is the value of the u-axis which is the horizontal axis of the image, the 0 x is the center point of the monocular camera, and the f is the focal length of the monocular camera.
9. A method of detecting an obstacle around a vehicle, comprising:
acquiring an image of the obstacle around the vehicle using a monocular camera;
reconstructing, by a controller, three-dimensional positions of corresponding points in the image, calculating, by the controller, a disparity value by applying a virtual baseline value formed by a movement of the monocular camera to a depth value obtained through three-dimensional reconstruction of the corresponding points, and estimating a boundary of the obstacle based on the disparity value by the controller; and
creating, by the controller, a distance based cost map, a color based cost map and an edge based cost map from the image, and estimating, by the controller, a height of the obstacle using the distance based cost map, the color based cost map, and the edge based cost map.
10. The method of claim 9 , wherein the disparity value is calculated as:
d
=
f
B
Z
,
where the ‘d’ is the disparity value to be obtained, the B is the virtual baseline value of the monocular camera, the Z is the depth value obtained through three-dimensional reconstruction, and the f is a focal length of the monocular camera.
11. The method of claim 9 , further comprising changing, when an angle of view of the monocular camera is a wide angle that is greater than or equal to a predetermined angle of view, a u-axis to an incident angular axis θu through the following equation:
θ
u
=
atan
(
u
-
o
x
f
)
,
where the u is the value of the u-axis which is the horizontal axis of the image, the 0 x is the center point of the monocular camera, and the f is the focal length of the monocular camera.
12. The method of claim 9 , wherein the creating of the distance based cost map comprises:
generating a plurality of Delaunay triangles by connecting feature points of the image and performing interpolation of distance information using a plane equation generated by three vertexes of each of the plurality of the Delaunay triangles; and
estimating a disparity of pixels included in each of the plurality of the Delaunay triangles from a result of the interpolation.
13. The method of claim 9 , wherein the creating of the color based cost map comprises:
setting an area where color similarity measurement is to be performed in the image, measuring color similarities between all feature points existing in the area, and selecting a greatest color similarity value as a final color similarity; and
creating the color based cost map by calculating a difference between sums of the color similarities from the measured color similarities.
14. The method of claim 9 , wherein the creating of the edge based cost map comprises:
performing edge detection on the image; and
performing distance transform on a result of the edge detection such that pixels located closer to edges have lower values.
15. The method of claim 9 , wherein a final cost map is calculated as:
c t =w d c d +w c c c +w e c e ,
where w d is a weight of the distance based cost map, w c is a weight of the color based cost map, w e is a weight of the edge based cost map, and c d is the distance based cost map, c c is the color based cost map, and edge c e is the edge based cost map.
16. A method of detecting an obstacle around a vehicle, comprising:
acquiring an image of the obstacle around the vehicle using a monocular camera; and
reconstructing, by a controller, three-dimensional positions of corresponding points in the image, calculating by the controller a disparity value by applying a virtual baseline value formed by a movement of the monocular camera to a depth value obtained through the three-dimensional reconstruction of the corresponding points, and estimating a boundary of the obstacle based on the disparity value by the controller.
17. A method of detecting an obstacle around a vehicle, comprising:
acquiring an image of the obstacle around the vehicle using a monocular camera; and
creating, by a controller, a distance based cost map, a color based cost map, and an edge based cost map from the image, and estimating by the controller a height of the obstacle using the distance based cost map, the color based cost map, and the edge based cost map.